{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,8]],"date-time":"2026-08-08T18:44:52Z","timestamp":1786214692709,"version":"3.56.0"},"reference-count":374,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2024,6,23]],"date-time":"2024-06-23T00:00:00Z","timestamp":1719100800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,6,23]],"date-time":"2024-06-23T00:00:00Z","timestamp":1719100800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Vis"],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1007\/s11263-024-02117-4","type":"journal-article","created":{"date-parts":[[2024,6,23]],"date-time":"2024-06-23T06:01:26Z","timestamp":1719122486000},"page":"5635-5662","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":480,"title":["Generalized Out-of-Distribution Detection: A Survey"],"prefix":"10.1007","volume":"132","author":[{"given":"Jingkang","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaiyang","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yixuan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4220-5958","authenticated-orcid":false,"given":"Ziwei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,6,23]]},"reference":[{"key":"2117_CR1","doi-asserted-by":"crossref","unstructured":"Abati, D., Porrello, A., Calderara, S., & Cucchiara, R. (2019). Latent space autoregression for novelty detection. In CVPR.","DOI":"10.1109\/CVPR.2019.00057"},{"key":"2117_CR2","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1007\/s11265-014-0913-0","volume":"79","author":"A Adler","year":"2015","unstructured":"Adler, A., Elad, M., Hel-Or, Y., & Rivlin, E. (2015). Sparse coding with anomaly detection. Journal of Signal Processing Systems, 79, 179\u2013188.","journal-title":"Journal of Signal Processing Systems"},{"key":"2117_CR3","doi-asserted-by":"crossref","unstructured":"Aggarwal, C. C., & Yu, P. S. (2001). Outlier detection for high dimensional data. In ACM SIGMOD.","DOI":"10.1145\/375663.375668"},{"key":"2117_CR4","doi-asserted-by":"crossref","unstructured":"Ahmed, F., & Courville, A. (2020). Detecting semantic anomalies. In AAAI.","DOI":"10.1609\/aaai.v34i04.5712"},{"key":"2117_CR5","doi-asserted-by":"crossref","first-page":"14410","DOI":"10.1109\/ACCESS.2018.2807385","volume":"6","author":"N Akhtar","year":"2018","unstructured":"Akhtar, N., & Mian, A. (2018). Threat of adversarial attacks on deep learning in computer vision: A survey. IEEE Access, 6, 14410\u201314430.","journal-title":"IEEE Access"},{"key":"2117_CR6","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1007\/s10618-014-0365-y","volume":"29","author":"L Akoglu","year":"2015","unstructured":"Akoglu, L., Tong, H., & Koutra, D. (2015). Graph based anomaly detection and description: A survey. Data Mining and Knowledge Discovery, 29, 626\u2013688.","journal-title":"Data Mining and Knowledge Discovery"},{"key":"2117_CR7","doi-asserted-by":"crossref","unstructured":"Al-Behadili, H., Grumpe, A., & W\u00f6hler, C. (2015). Incremental learning and novelty detection of gestures in a multi-class system. In AIMS.","DOI":"10.1109\/CGVIS.2015.7449915"},{"key":"2117_CR8","first-page":"66","volume":"6","author":"DG Altman","year":"2005","unstructured":"Altman, D. G., & Bland, J. M. (2005). Standard deviations and standard errors. BMJ, 6, 66.","journal-title":"BMJ"},{"key":"2117_CR9","unstructured":"Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Man\u00e9, D. (2016). Concrete problems in AI safety, arXiv preprint arXiv:1606.06565"},{"key":"2117_CR10","unstructured":"An, J., & Cho, S. (2015). Variational autoencoder based anomaly detection using reconstruction probability. In Special lecture on IE."},{"key":"2117_CR11","unstructured":"Angelopoulos, A. N., & Bates, S. (2021). A gentle introduction to conformal prediction and distribution-free uncertainty quantification, arXiv preprint arXiv:2107.07511"},{"key":"2117_CR12","doi-asserted-by":"crossref","first-page":"1110","DOI":"10.1177\/1475921717737051","volume":"17","author":"DJ Atha","year":"2018","unstructured":"Atha, D. J., & Jahanshahi, M. R. (2018). Evaluation of deep learning approaches based on convolutional neural networks for corrosion detection. Structural Health Monitoring, 17, 1110\u20131128.","journal-title":"Structural Health Monitoring"},{"key":"2117_CR13","doi-asserted-by":"crossref","unstructured":"Averly, R., & Chao, W.-L. (2023). Unified out-of-distribution detection: A model-specific perspective, arXiv preprint arXiv:2304.06813","DOI":"10.1109\/ICCV51070.2023.00140"},{"key":"2117_CR14","doi-asserted-by":"crossref","unstructured":"Bai, Y., Han, Z., Zhang, C., Cao, B., Jiang, X., & Hu, Q. (2023). Id-like prompt learning for few-shot out-of-distribution detection, arXiv preprint arXiv:2311.15243","DOI":"10.1109\/CVPR52733.2024.01655"},{"key":"2117_CR15","first-page":"8","volume":"9","author":"PL Bartlett","year":"2008","unstructured":"Bartlett, P. L., & Wegkamp, M. H. (2008). Classification with a reject option using a hinge loss. Journal of Machine Learning Research, 9, 8.","journal-title":"Journal of Machine Learning Research"},{"key":"2117_CR16","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/s10115-006-0026-6","volume":"11","author":"S Basu","year":"2007","unstructured":"Basu, S., & Meckesheimer, M. (2007). Automatic outlier detection for time series: An application to sensor data. Knowledge and Information Systems, 11, 137\u2013154.","journal-title":"Knowledge and Information Systems"},{"key":"2117_CR17","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1007\/s10994-020-05877-5","volume":"109","author":"J Bekker","year":"2020","unstructured":"Bekker, J., & Davis, J. (2020). Learning from positive and unlabeled data: A survey. Machine Learning, 109, 719\u2013760.","journal-title":"Machine Learning"},{"key":"2117_CR18","doi-asserted-by":"crossref","unstructured":"Bendale, A., & Boult, T. (2015). Towards open world recognition. In CVPR.","DOI":"10.1109\/CVPR.2015.7298799"},{"key":"2117_CR19","doi-asserted-by":"crossref","unstructured":"Bendale, A., & Boult, T. E. (2016). Towards open set deep networks. In CVPR.","DOI":"10.1109\/CVPR.2016.173"},{"key":"2117_CR20","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1007\/s10994-009-5152-4","volume":"79","author":"S Ben-David","year":"2010","unstructured":"Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., & Vaughan, J. W. (2010). A theory of learning from different domains. Machine Learning, 79, 151\u2013175.","journal-title":"Machine Learning"},{"key":"2117_CR21","doi-asserted-by":"crossref","unstructured":"Ben-Gal, I. (2005). Outlier detection. In Data mining and knowledge discovery handbook.","DOI":"10.1007\/0-387-25465-X_7"},{"key":"2117_CR22","unstructured":"Bergman, L., & Hoshen, Y. (2020). Classification-based anomaly detection for general data. In ICLR."},{"key":"2117_CR23","doi-asserted-by":"crossref","unstructured":"Bergmann, P., Fauser, M., Sattlegger, D., & Steger, C. (2019). Mvtec ad\u2014A comprehensive real-world dataset for unsupervised anomaly detection. In CVPR.","DOI":"10.1109\/CVPR.2019.00982"},{"key":"2117_CR24","doi-asserted-by":"crossref","unstructured":"Bianchini, M., Belahcen, A., & Scarselli, F. (2016). A comparative study of inductive and transductive learning with feedforward neural networks. In Conference of the Italian Association for artificial intelligence.","DOI":"10.1007\/978-3-319-49130-1_21"},{"key":"2117_CR25","unstructured":"Bibas, K., Feder, M., & Hassner, T. (2021). Single layer predictive normalized maximum likelihood for out-of-distribution detection. In NeurIPS."},{"key":"2117_CR26","unstructured":"Bitterwolf, J., Meinke, A., & Hein, M. (2020). Certifiably adversarially robust detection of out-of-distribution data. In NeurIPS."},{"key":"2117_CR27","unstructured":"Bitterwolf, J., M\u00fcller, M., & Hein, M. (2023). In or out? fixing imagenet out-of-distribution detection evaluation. In ICML."},{"key":"2117_CR28","doi-asserted-by":"crossref","unstructured":"Bodesheim, P., Freytag, A., Rodner, E., Kemmler, M., & Denzler, J. (2013). Kernel null space methods for novelty detection. In CVPR.","DOI":"10.1109\/CVPR.2013.433"},{"key":"2117_CR29","unstructured":"Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., & Brynjolfsson, E. (2021). On the opportunities and risks of foundation models, arXiv preprint arXiv:2108.07258"},{"key":"2117_CR30","doi-asserted-by":"crossref","unstructured":"Boult, T. E., Cruz, S., Dhamija, A. R., Gunther, M., Henrydoss, J., & Scheirer, W. J. (2019). Learning and the unknown: Surveying steps toward open world recognition. In AAAI.","DOI":"10.1609\/aaai.v33i01.33019801"},{"key":"2117_CR31","doi-asserted-by":"crossref","unstructured":"Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000). Lof: identifying density-based local outliers. In SIGMOD.","DOI":"10.1145\/342009.335388"},{"key":"2117_CR32","doi-asserted-by":"crossref","first-page":"132330","DOI":"10.1109\/ACCESS.2020.3010274","volume":"8","author":"S Bulusu","year":"2020","unstructured":"Bulusu, S., Kailkhura, B., Li, B., Varshney, P. K., & Song, D. (2020). Anomalous example detection in deep learning: A survey. IEEE Access, 8, 132330\u2013132347.","journal-title":"IEEE Access"},{"key":"2117_CR33","doi-asserted-by":"crossref","unstructured":"Cai, F., Ozdagli, A. I., Potteiger, N., & Koutsoukos, X. (2021). Inductive conformal out-of-distribution detection based on adversarial autoencoders. In 2021 IEEE international conference on omni-layer intelligent systems (COINS) (pp. 1\u20136). IEEE.","DOI":"10.1109\/COINS51742.2021.9524167"},{"key":"2117_CR34","doi-asserted-by":"crossref","unstructured":"Cao, A., Luo, Y., & Klabjan, D. (2020). Open-set recognition with Gaussian mixture variational autoencoders. In AAAI.","DOI":"10.1609\/aaai.v35i8.16848"},{"key":"2117_CR35","unstructured":"Cao, K., Brbic, M., & Leskovec, J. (2021). Open-world semi-supervised learning, arXiv preprint arXiv:2102.03526"},{"key":"2117_CR36","unstructured":"Castillo, E. (2012). Extreme value theory in engineering. Elsevier."},{"key":"2117_CR37","unstructured":"Chalapathy, R., & Chawla, S. (2019). Deep learning for anomaly detection: A survey, arXiv preprint arXiv:1901.0340"},{"issue":"3","key":"2117_CR38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1541880.1541882","volume":"41","author":"V Chandola","year":"2009","unstructured":"Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM computing surveys (CSUR), 41(3), 1\u201358.","journal-title":"ACM computing surveys (CSUR)"},{"key":"2117_CR39","doi-asserted-by":"crossref","unstructured":"Chen, G., Peng, P., Ma, L., Li, J., Du, L., & Tian, Y. (2021a). Amplitude-phase recombination: Rethinking robustness of convolutional neural networks in frequency domain. In ICCV.","DOI":"10.1109\/ICCV48922.2021.00051"},{"key":"2117_CR40","doi-asserted-by":"crossref","unstructured":"Chen, G., Qiao, L., Shi, Y., Peng, P., Li, J., Huang, T., Pu, S., & Tian, Y. (2020a). Learning open set network with discriminative reciprocal points. In ECCV.","DOI":"10.1007\/978-3-030-58580-8_30"},{"key":"2117_CR41","unstructured":"Chen, J., Li, Y., Wu, X., Liang, Y., & Jha, S. (2020b). Robust out-of-distribution detection for neural networks, arXiv preprint arXiv:2003.09711"},{"key":"2117_CR42","doi-asserted-by":"crossref","unstructured":"Chen, J., Li, Y., Wu, X., Liang, Y., & Jha, S. (2021c). Atom: Robustifying out-of-distribution detection using outlier mining. In ECML &PKDD.","DOI":"10.1007\/978-3-030-86523-8_26"},{"key":"2117_CR43","doi-asserted-by":"crossref","unstructured":"Chen, X., & Gupta, A. (2015). Webly supervised learning of convolutional networks. In ICCV.","DOI":"10.1109\/ICCV.2015.168"},{"key":"2117_CR44","doi-asserted-by":"crossref","unstructured":"Chen, X., Lan, X., Sun, F., & Zheng, N. (2020c). A boundary based out-of-distribution classifier for generalized zero-shot learning. In ECCV.","DOI":"10.1007\/978-3-030-58586-0_34"},{"key":"2117_CR45","doi-asserted-by":"crossref","unstructured":"Chen, Z., Yeo, C. K., Lee, B. S., & Lau, C. T. (2018). Autoencoder-based network anomaly detection. In Wireless telecommunications symposium.","DOI":"10.1109\/WTS.2018.8363930"},{"key":"2117_CR46","unstructured":"Choi, H., Jang, E., & Alemi, A. A. (2018). Waic, but why? generative ensembles for robust anomaly detection, arXiv preprint arXiv:1810.01392"},{"key":"2117_CR47","unstructured":"Choi, S., & Chung, S.-Y. (2020). Novelty detection via blurring. In ICLR."},{"key":"2117_CR48","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1109\/TIT.1970.1054406","volume":"16","author":"C Chow","year":"1970","unstructured":"Chow, C. (1970). On optimum recognition error and reject tradeoff. IEEE Transactions on Information Theory, 16, 41\u20136.","journal-title":"IEEE Transactions on Information Theory"},{"key":"2117_CR49","doi-asserted-by":"crossref","unstructured":"Chu, W.-H., & Kitani, K. M. (2020). Neural batch sampling with reinforcement learning for semi-supervised anomaly detection. In ECCV.","DOI":"10.1007\/978-3-030-58574-7_45"},{"key":"2117_CR50","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20, 273\u201397.","journal-title":"Machine learning"},{"key":"2117_CR51","doi-asserted-by":"crossref","unstructured":"Cultrera, L., Seidenari, L., & Del Bimbo, A. (2023). Leveraging visual attention for out-of-distribution detection. In Proceedings of the IEEE\/CVF international conference on computer vision (pp. 4447\u20134456).","DOI":"10.1109\/ICCVW60793.2023.00479"},{"key":"2117_CR52","doi-asserted-by":"crossref","unstructured":"Dai, Y., Lang, H., Zeng, K., Huang, F., & Li, Y., (2023). Exploring large language models for multi-modal out-of-distribution detection, arXiv preprint arXiv:2310.08027","DOI":"10.18653\/v1\/2023.findings-emnlp.351"},{"key":"2117_CR53","doi-asserted-by":"crossref","unstructured":"Danuser, G., & Stricker, M. (1998). Parametric model fitting: From inlier characterization to outlier detection. In TPAMI.","DOI":"10.1109\/34.667884"},{"key":"2117_CR54","doi-asserted-by":"crossref","unstructured":"De Maesschalck, R., Jouan-Rimbaud, D., & Massart, D. L. (2000). The Mahalanobis distance, chemometrics and intelligent laboratory systems.","DOI":"10.1016\/S0169-7439(99)00047-7"},{"key":"2117_CR55","doi-asserted-by":"crossref","unstructured":"Deecke, L., Vandermeulen, R., Ruff, L., Mandt, S., & Kloft, M. (2018). Image anomaly detection with generative adversarial networks. In ECML &KDD.","DOI":"10.1007\/978-3-030-10925-7_1"},{"key":"2117_CR56","unstructured":"Denouden, T., Salay, R., Czarnecki, K., Abdelzad, V., Phan, B., & Vernekar, S. (2018). Improving reconstruction autoencoder out-of-distribution detection with Mahalanobis distance, arXiv preprint arXiv:1812.02765"},{"key":"2117_CR57","doi-asserted-by":"crossref","unstructured":"Desforges, M., Jacob, P., & Cooper, J. (1998). Applications of probability density estimation to the detection of abnormal conditions in engineering. In Proceedings of the institution of mechanical engineers.","DOI":"10.1243\/0954406981521448"},{"key":"2117_CR58","unstructured":"DeVries, T., & Taylor, G. W. (2017). Improved regularization of convolutional neural networks with cutout, arXiv preprint arXiv:1708.04552"},{"key":"2117_CR59","unstructured":"DeVries, T., & Taylor, G. W. (2018). Learning confidence for out-of-distribution detection in neural networks, arXiv preprint arXiv:1802.04865"},{"key":"2117_CR60","unstructured":"Dhamija, A. R., G\u00fcnther, M., & Boult, T. E. (2018). Reducing network agnostophobia. In NeurIPS."},{"key":"2117_CR61","doi-asserted-by":"crossref","unstructured":"Diehl, C. P., & Hampshire, J. B. (2002). Real-time object classification and novelty detection for collaborative video surveillance. In IJCNN.","DOI":"10.1109\/IJCNN.2002.1007557"},{"key":"2117_CR62","doi-asserted-by":"crossref","unstructured":"Dietterich, T. G. (2000). Ensemble methods in machine learning. In International workshop on multiple classifier systems.","DOI":"10.1007\/3-540-45014-9_1"},{"key":"2117_CR63","unstructured":"Djurisic, A., Bozanic, N., Ashok, A., & Liu, R. (2023). Extremely simple activation shaping for out-of-distribution detection. In ICLR."},{"key":"2117_CR64","unstructured":"Dolhansky, B., Howes, R., Pflaum, B., Baram, N., & Ferrer, C. C. (2019). The deepfake detection challenge (dfdc) preview dataset, arXiv preprint arXiv:1910.08854"},{"key":"2117_CR65","unstructured":"Dong, J., Gao, Y., Zhou, H., Cen, J., Yao, Y., Yoon, S., & Sun, P. D. (2023). Towards few-shot out-of-distribution detection, arXiv preprint arXiv:2311.12076"},{"key":"2117_CR66","doi-asserted-by":"crossref","unstructured":"Dong, X., Guo, J., Ang Li, W.-T.T., Liu, C., & Kung, H. (2022a). Neural mean discrepancy for efficient out-of-distribution detection. In CVPR.","DOI":"10.1109\/CVPR52688.2022.01862"},{"key":"2117_CR67","doi-asserted-by":"crossref","unstructured":"Dong, X., Guo, J., Li, A., Ting, W.-T., Liu, C., & Kung, H. (2022a). Neural mean discrepancy for efficient out-of-distribution detection. In CVPR.","DOI":"10.1109\/CVPR52688.2022.01862"},{"key":"2117_CR68","doi-asserted-by":"crossref","unstructured":"Dou, Y., Li, W., Liu, Z., Dong, Z., Luo, J., & Philip, S. Y. (2019). Uncovering download fraud activities in mobile app markets. In ASONAM.","DOI":"10.1145\/3341161.3345306"},{"key":"2117_CR69","unstructured":"Drummond, N., & Shearer, R. (2006). The open world assumption. In eSI workshop."},{"key":"2117_CR70","doi-asserted-by":"crossref","unstructured":"Du, X., Wang, X., Gozum, G., & Li, Y. (2022a). Unknown-aware object detection: Learning what you don\u2019t know from videos in the wild. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition.","DOI":"10.1109\/CVPR52688.2022.01331"},{"key":"2117_CR71","unstructured":"Du, X., Wang, Z., Cai, M., & Li, Y. (2022b). Vos: Learning what you don\u2019t know by virtual outlier synthesis. In Proceedings of the international conference on learning representations."},{"key":"2117_CR72","unstructured":"Eskin, E. (2000). Anomaly detection over noisy data using learned probability distributions. In ICML."},{"key":"2117_CR73","doi-asserted-by":"crossref","unstructured":"Esmaeilpour, S., Liu, B., Robertson, E., & Shu, L. (2022). Zero-shot out-of-distribution detection based on the pretrained model clip. In AAAI.","DOI":"10.1609\/aaai.v36i6.20610"},{"key":"2117_CR74","unstructured":"Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In KDD."},{"key":"2117_CR75","unstructured":"Fang, Z., Li, Y., Lu, J., Dong, J., Han, B., & Liu, F. (2022). Is out-of-distribution detection learnable? In NeurIPS."},{"key":"2117_CR76","unstructured":"Fang, Z., Lu, J., Liu, A., Liu, F., & Zhang, G. (2021). Learning bounds for open-set learning. In ICML."},{"key":"2117_CR77","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","volume":"27","author":"T Fawcett","year":"2006","unstructured":"Fawcett, T. (2006). An introduction to roc analysis. Pattern Recognition Letters, 27, 861\u201374.","journal-title":"Pattern Recognition Letters"},{"key":"2117_CR78","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1145\/358669.358692","volume":"24","author":"MA Fischler","year":"1981","unstructured":"Fischler, M. A., & Bolles, R. C. (1981). Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24, 381\u2013395.","journal-title":"Communications of the ACM"},{"key":"2117_CR79","unstructured":"Foong, A. Y., Li, Y., Hern\u00e1ndez-Lobato, J. M., & Turner, R. E. (2020). \u2019in-between\u2019 uncertainty in Bayesian neural networks. In ICML-W."},{"key":"2117_CR80","unstructured":"Fort, S., Ren, J., & Lakshminarayanan, B. (2021). Exploring the limits of out-of-distribution detection. In NeurIPS."},{"key":"2117_CR81","doi-asserted-by":"crossref","unstructured":"Fumera, G., & Roli, F. (2002). Support vector machines with embedded reject option. In International workshop on support vector machines.","DOI":"10.1007\/3-540-45665-1_6"},{"key":"2117_CR82","unstructured":"Gal, Y., & Ghahramani, Z. (2016). Dropout as a Bayesian approximation: Representing model uncertainty in deep learning. In ICML."},{"key":"2117_CR83","doi-asserted-by":"crossref","unstructured":"Gamerman, D., & Lopes, H. F. (2006). Markov chain Monte Carlo: Stochastic simulation for Bayesian inference. CRC Press.","DOI":"10.1201\/9781482296426"},{"key":"2117_CR84","unstructured":"Gan, W. (2021). Language guided out-of-distribution detection."},{"key":"2117_CR85","first-page":"1","volume":"132","author":"P Gao","year":"2023","unstructured":"Gao, P., Geng, S., Zhang, R., Ma, T., Fang, R., Zhang, Y., Li, H., & Qiao, Y. (2023). Clip-adapter: Better vision-language models with feature adapters. International Journal of Computer Vision, 132, 1\u201315.","journal-title":"International Journal of Computer Vision"},{"key":"2117_CR86","doi-asserted-by":"crossref","unstructured":"Gatys, L. A., Ecker, A. S., & Bethge, M. (2016). Image style transfer using convolutional neural networks. In CVPR.","DOI":"10.1109\/CVPR.2016.265"},{"key":"2117_CR87","doi-asserted-by":"crossref","unstructured":"Ge, Z., Demyanov, S., Chen, Z., & Garnavi, R. (2017). Generative openmax for multi-class open set classification. In BMVC.","DOI":"10.5244\/C.31.42"},{"key":"2117_CR88","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., & Urtasun, R. (2012). Are we ready for autonomous driving? The Kitti vision benchmark suite. In CVPR.","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"2117_CR89","first-page":"4445","volume":"66","author":"A Gelman","year":"2008","unstructured":"Gelman, A. (2008). Objections to Bayesian statistics. Bayesian Analysis, 66, 4445\u2013449.","journal-title":"Bayesian Analysis"},{"key":"2117_CR90","unstructured":"Geng, C., & Chen, S. (2020). Collective decision for open set recognition. In TKDE."},{"key":"2117_CR91","unstructured":"Geng, C., Huang, S., & Chen, S. (2020). Recent advances in open set recognition: A survey. In TPAMI."},{"key":"2117_CR92","doi-asserted-by":"crossref","unstructured":"Georgescu, M.-I., Barbalau, A., Ionescu, R. T., Khan, F. S., Popescu, M., & Shah, M. (2021). Anomaly detection in video via self-supervised and multi-task learning. In CVPR.","DOI":"10.1109\/CVPR46437.2021.01255"},{"key":"2117_CR93","unstructured":"Golan, I., & El-Yaniv, R. (2018). Deep anomaly detection using geometric transformations. In NeurIPS."},{"key":"2117_CR94","unstructured":"Goldstein, M., & Dengel, A. (2012). Histogram-based outlier score (hbos): A fast unsupervised anomaly detection algorithm. In KI-2012: Poster and demo track."},{"key":"2117_CR95","unstructured":"Gomes, E. D. C., Alberge, F., Duhamel, P., & Piantanida, P. (2022). Igeood: An information geometry approach to out-of-distribution detection. In ICLR."},{"key":"2117_CR96","doi-asserted-by":"crossref","unstructured":"Gong, D., Liu, L., Le, V., Saha, B., Mansour, M. R., Venkatesh, S., & Hengel, A. V. D. (2019). Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection. In CVPR.","DOI":"10.1109\/ICCV.2019.00179"},{"key":"2117_CR97","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. In NIPS."},{"key":"2117_CR98","unstructured":"Goodfellow, I. J., Shlens, J., & Szegedy, C. (2015). Explaining and harnessing adversarial examples. In ICLR."},{"key":"2117_CR99","unstructured":"Han, B., Yao, Q., Yu, X., Niu, G., Xu, M., Hu, W., Tsang, I., & Sugiyama, M. (2018). Co-teaching: Robust training of deep neural networks with extremely noisy labels. In NIPS."},{"key":"2117_CR100","doi-asserted-by":"crossref","unstructured":"Han, K., Vedaldi, A., & Zisserman, A. (2019). Learning to discover novel visual categories via deep transfer clustering. In CVPR.","DOI":"10.1109\/ICCV.2019.00849"},{"key":"2117_CR101","unstructured":"Han, X., Chen, X., & Liu, L.-P. (2020). Gan ensemble for anomaly detection, arXiv preprint arXiv:2012.07988"},{"key":"2117_CR102","doi-asserted-by":"crossref","unstructured":"Hautamaki, V., Karkkainen, I., & Franti, P. (2004). Outlier detection using k-nearest neighbour graph. In ICPR.","DOI":"10.1109\/ICPR.2004.1334558"},{"key":"2117_CR103","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., & Sun, J. (2015). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In ICCV.","DOI":"10.1109\/ICCV.2015.123"},{"key":"2117_CR104","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In CVPR.","DOI":"10.1109\/CVPR.2016.90"},{"key":"2117_CR105","doi-asserted-by":"crossref","unstructured":"Hein, M., Andriushchenko, M., & Bitterwolf, J. (2019). Why relu networks yield high-confidence predictions far away from the training data and how to mitigate the problem. In CVPR.","DOI":"10.1109\/CVPR.2019.00013"},{"key":"2117_CR106","unstructured":"Hendrycks, D., Basart, S., Mazeika, M., Mostajabi, M., Steinhardt, J., & Song, D. (2022a) Scaling out-of-distribution detection for real-world settings. In ICML."},{"key":"2117_CR107","unstructured":"Hendrycks, D., Carlini, N., Schulman, J., & Steinhardt, J. (2021). Unsolved problems in ML safety. arXiv preprint, arXiv:2109.13916"},{"key":"2117_CR108","unstructured":"Hendrycks, D., & Gimpel, K. (2017). A baseline for detecting misclassified and out-of-distribution examples in neural networks. In ICLR."},{"key":"2117_CR109","unstructured":"Hendrycks, D., Lee, K., & Mazeika, M. (2019a). Using pre-training can improve model robustness and uncertainty. In International conference on machine learning (pp. 2712\u20132721). PMLR."},{"key":"2117_CR110","doi-asserted-by":"crossref","unstructured":"Hendrycks, D., Liu, X., Wallace, E., Dziedzic, A., Krishnan, R., & Song, D. (2020). Pretrained transformers improve out-of-distribution robustness, arXiv preprint arXiv:2004.06100","DOI":"10.18653\/v1\/2020.acl-main.244"},{"key":"2117_CR111","unstructured":"Hendrycks, D., & Mazeika, M. (2022). X-risk analysis for AI research. arXiv preprint, arXiv:2206.05862"},{"key":"2117_CR112","unstructured":"Hendrycks, D., Mazeika, M., & Dietterich, T. (2019b). Deep anomaly detection with outlier exposure. In ICLR."},{"key":"2117_CR113","unstructured":"Hendrycks, D., Mu, N., Cubuk, E. D., Zoph, B., Gilmer, J., & Lakshminarayanan, B. (2019c). Augmix: A simple data processing method to improve robustness and uncertainty. arXiv preprint arXiv:1912.02781"},{"key":"2117_CR114","doi-asserted-by":"crossref","unstructured":"Hendrycks, D., Zou, A., Mazeika, M., Tang, L., Song, D., & Steinhardt, J. (2022c). Pixmix: Dreamlike pictures comprehensively improve safety measures.","DOI":"10.1109\/CVPR52688.2022.01628"},{"key":"2117_CR115","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1023\/B:AIRE.0000045502.10941.a9","volume":"22","author":"V Hodge","year":"2004","unstructured":"Hodge, V., & Austin, J. (2004). A survey of outlier detection methodologies. Artificial Intelligence Review, 22, 85\u2013126.","journal-title":"Artificial Intelligence Review"},{"key":"2117_CR116","doi-asserted-by":"crossref","unstructured":"Hsu, Y.-C., Shen, Y., Jin, H., & Kira, Z. (2020). Generalized odin: Detecting out-of-distribution image without learning from out-of-distribution data. In CVPR.","DOI":"10.1109\/CVPR42600.2020.01096"},{"key":"2117_CR117","unstructured":"Hu, W., Gao, J., Li, B., Wu, O., Du, J., & Maybank, S. (2018). Anomaly detection using local kernel density estimation and context-based regression. In TKDE."},{"key":"2117_CR118","unstructured":"Huang, H., Li, Z., Wang, L., Chen, S., Dong, B., & Zhou, X. (2020a). Feature space singularity for out-of-distribution detection, arXiv preprint arXiv:2011.14654"},{"key":"2117_CR119","unstructured":"Huang, R., Geng, A., & Li, Y. (2021). On the importance of gradients for detecting distributional shifts in the wild. In NeurIPS."},{"key":"2117_CR120","doi-asserted-by":"crossref","unstructured":"Huang, R., & Li, Y. (2021). Mos: Towards scaling out-of-distribution detection for large semantic space. In CVPR.","DOI":"10.1109\/CVPR46437.2021.00860"},{"key":"2117_CR121","doi-asserted-by":"crossref","unstructured":"Huang, X., Kroening, D., Ruan, W., Sharp, J., Sun, Y., Thamo, E., Wu, M., & Yi, X. (2020b). A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability. Computer Science Review, 37, 100270.","DOI":"10.1016\/j.cosrev.2020.100270"},{"key":"2117_CR122","unstructured":"Hyv\u00e4rinen, A., & Dayan, P. (2005). Estimation of non-normalized statistical models by score matching."},{"key":"2117_CR123","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1108\/PIJPSM-11-2016-0158","volume":"41","author":"H Idrees","year":"2018","unstructured":"Idrees, H., Shah, M., & Surette, R. (2018). Enhancing camera surveillance using computer vision: A research note. Policing: An International Journal, 41, 292\u2013307.","journal-title":"Policing: An International Journal"},{"key":"2117_CR124","unstructured":"Igoe, C., Chung, Y., Char, I., & Schneider, J. (2022). How useful are gradients for ood detection really? arXiv preprint arXiv:2205.10439"},{"key":"2117_CR125","first-page":"205","volume":"86","author":"AJ Izenman","year":"1991","unstructured":"Izenman, A. J. (1991). Review papers: Recent developments in nonparametric density estimation. Journal of the American Statistical Association, 86, 205\u2013224.","journal-title":"Journal of the American Statistical Association"},{"key":"2117_CR126","doi-asserted-by":"crossref","unstructured":"Jain, L. P., Scheirer, W. J., & Boult, T. E. (2014). Multi-class open set recognition using probability of inclusion. In ECCV.","DOI":"10.1007\/978-3-319-10578-9_26"},{"key":"2117_CR127","unstructured":"Jang, J., & Kim, C. O. (2020). One-vs-rest network-based deep probability model for open set recognition, arXiv preprint arXiv:2004.08067"},{"key":"2117_CR128","doi-asserted-by":"crossref","unstructured":"Jaskie, K., & Spanias, A. (2019). Positive and unlabeled learning algorithms and applications: A survey. In International conference on information, intelligence, systems and applications.","DOI":"10.1109\/IISA.2019.8900698"},{"key":"2117_CR129","doi-asserted-by":"crossref","unstructured":"Jaynes, E. T. (1986). Bayesian methods: General background.","DOI":"10.1017\/CBO9780511569678.003"},{"key":"2117_CR130","unstructured":"Jeong, T., & Kim, H. (2020). Ood-maml: Meta-learning for few-shot out-of-distribution detection and classification. In NeurIPS."},{"key":"2117_CR131","doi-asserted-by":"crossref","unstructured":"Jia, M., Tang, L., Chen, B.-C., Cardie, C., Belongie, S., Hariharan, B., & Lim, S.-N. (2022). Visual prompt tuning. In European conference on computer vision (pp. 709\u2013727). Springer.","DOI":"10.1007\/978-3-031-19827-4_41"},{"key":"2117_CR132","doi-asserted-by":"crossref","unstructured":"Jia, X., Han, K., Zhu, Y., & Green, B. (2021). Joint representation learning and novel category discovery on single-and multi-modal data. In ICCV.","DOI":"10.1109\/ICCV48922.2021.00065"},{"key":"2117_CR133","unstructured":"Jiang, D., Sun, S., & Yu, Y. (2021a). Revisiting flow generative models for out-of-distribution detection. In International conference on learning representations."},{"key":"2117_CR134","doi-asserted-by":"crossref","unstructured":"Jiang, K., Xie, W., Lei, J., Jiang, T., & Li, Y. (2021b). Lren: Low-rank embedded network for sample-free hyperspectral anomaly detection. In AAAI.","DOI":"10.1609\/aaai.v35i5.16536"},{"key":"2117_CR135","unstructured":"Jiang, L., Guo, Z., Wu, W., Liu, Z., Liu, Z., Loy, C.C., Yang, S., Xiong, Y., Xia, W., Chen, B., Zhuang, P., Li, S., Chen, S., Yao, T., Ding, S., Li, J., Huang, F., Cao, L., Ji, R., Lu, C., & Tan, G. (2021c). DeeperForensics Challenge 2020 on real-world face forgery detection: Methods and results, arXiv preprint arXiv:2102.09471"},{"key":"2117_CR136","doi-asserted-by":"crossref","unstructured":"Jiang, W., Cheng, H., Chen, M., Feng, S., Ge, Y., & Wang, C. (2023a). Read: Aggregating reconstruction error into out-of-distribution detection. In AAAI.","DOI":"10.1609\/aaai.v37i12.26741"},{"key":"2117_CR137","unstructured":"Jiang, X., Liu, F., Fang, Z., Chen, H., Liu, T., Zheng, F., & Han, B. (2023b). Detecting out-of-distribution data through in-distribution class prior. In International conference on machine learning (pp. 15067\u201315088). PMLR."},{"key":"2117_CR138","unstructured":"Jiang, X., Liu, F., Fang, Z., Chen, H., Liu, T., Zheng, F., & Han, B. (2023c). Negative label guided ood detection with pretrained vision-language models. In The twelfth international conference on learning representations."},{"key":"2117_CR139","doi-asserted-by":"crossref","unstructured":"Joseph, K., Paul, S., Aggarwal, G., Biswas, S., Rai, P., Han, K., & Balasubramanian, V. N. (2022). Novel class discovery without forgetting. In ECCV.","DOI":"10.1007\/978-3-031-20053-3_33"},{"key":"2117_CR140","unstructured":"J\u00fanior, P.R.M., De Souza, R. M., Werneck, R. D. O., Stein, B. V., Pazinato, D. V., de Almeida, W. R., Penatti, O. A., Torres, R. D. S., & Rocha, A. (2017). Nearest neighbors distance ratio open-set classifier. Machine Learning, 6, 66."},{"key":"2117_CR141","unstructured":"Katz-Samuels, J., Nakhleh, J., Nowak, R., & Li, Y. (2022). Training ood detectors in their natural habitats. In International conference on machine learning (ICML). PMLR."},{"key":"2117_CR142","doi-asserted-by":"crossref","unstructured":"Kaur, R., Jha, S., Roy, A., Park, S., Dobriban, E., Sokolsky, O., & Lee, I. (2022a). idecode: In-distribution equivariance for conformal out-of-distribution detection. In Proceedings of the AAAI conference on artificial intelligence (vol. 36, pp. 7104\u20137114).","DOI":"10.1609\/aaai.v36i7.20670"},{"key":"2117_CR143","doi-asserted-by":"crossref","unstructured":"Kaur, R., Sridhar, K., Park, S., Jha, S., Roy, A., Sokolsky, O., & Lee, I. (2022b). Codit: Conformal out-of-distribution detection in time-series data, arXiv e-prints.","DOI":"10.1145\/3576841.3585931"},{"key":"2117_CR144","doi-asserted-by":"crossref","unstructured":"Kerner, H. R., Wellington, D. F., Wagstaff, K. L., Bell, J. F., Kwan, C., & Amor, H. B. (2019). Novelty detection for multispectral images with application to planetary exploration. In AAAI.","DOI":"10.1609\/aaai.v33i01.33019484"},{"key":"2117_CR145","unstructured":"Kim, J.-H., Yun, S., & Song, H. O. (2023). Neural relation graph: A unified framework for identifying label noise and outlier data. In Thirty-seventh conference on neural information processing systems."},{"key":"2117_CR146","unstructured":"Kim, K., Shin, J., & Kim, H. (2021). Locally most powerful Bayesian test for out-of-distribution detection using deep generative models. In NeurIPS."},{"key":"2117_CR147","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1109\/TNSM.2009.090604","volume":"6","author":"A Kind","year":"2009","unstructured":"Kind, A., Stoecklin, M. P., & Dimitropoulos, X. (2009). Histogram-based traffic anomaly detection. IEEE Transactions on Network and Service Management, 6, 110\u2013121.","journal-title":"IEEE Transactions on Network and Service Management"},{"key":"2117_CR148","unstructured":"Kingma, D. P., & Dhariwal, P. (2018). Glow: Generative flow with invertible 1x1 convolutions. In NeurIPS."},{"key":"2117_CR149","unstructured":"Kingma, D. P., & Welling, M. (2013). Auto-encoding variational Bayes, arXiv preprint arXiv:1312.6114"},{"key":"2117_CR150","unstructured":"Kirichenko, P., Izmailov, P., & Wilson, A. G. (2020). Why normalizing flows fail to detect out-of-distribution data. in NeurIPS."},{"key":"2117_CR151","unstructured":"Kobyzev, I., Prince, S., & Brubaker, M. (2020). Normalizing flows: An introduction and review of current methods. In TPAMI."},{"key":"2117_CR152","unstructured":"Koh, P. W., Sagawa, S., Marklund, H., Xie, S. M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R. L., Gao, I., & Lee, T. (2021). Wilds: A benchmark of in-the-wild distribution shifts. In International conference on machine learning (pp. 5637\u20135664). PMLR."},{"key":"2117_CR153","doi-asserted-by":"crossref","unstructured":"Kong, S., & Ramanan, D. (2021). Opengan: Open-set recognition via open data generation. In ICCV.","DOI":"10.1109\/ICCV48922.2021.00085"},{"key":"2117_CR154","doi-asserted-by":"crossref","unstructured":"Kou, Y., Lu, C.-T., & Dos Santos, R. F. (2007). Spatial outlier detection: A graph-based approach. In 19th IEEE international conference on tools with artificial intelligence (ICTAI).","DOI":"10.1109\/ICTAI.2007.139"},{"key":"2117_CR155","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1002\/aic.690370209","volume":"37","author":"MA Kramer","year":"1991","unstructured":"Kramer, M. A. (1991). Nonlinear principal component analysis using autoassociative neural networks. AIChE Journal, 37, 233\u2013243.","journal-title":"AIChE Journal"},{"key":"2117_CR156","unstructured":"Krizhevsky, A., & Hinton, G. (2009). Learning multiple layers of features from tiny images."},{"key":"2117_CR157","unstructured":"Krizhevsky, A., Nair, V., & Hinton, G. (2009). Cifar-10 and cifar-100 datasets (vol. 6, (no. 1), p. 1). https:\/\/www.cs.toronto.edu\/kriz\/cifar.html"},{"key":"2117_CR158","unstructured":"Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In NIPS."},{"key":"2117_CR159","doi-asserted-by":"crossref","unstructured":"Kwon, G., Prabhushankar, M., Temel, D., & AlRegib, G. (2020). Backpropagated gradient representations for anomaly detection. In ECCV.","DOI":"10.1007\/978-3-030-58589-1_13"},{"key":"2117_CR160","unstructured":"Kylberg, G. (2011). Kylberg texture dataset v. 1.0."},{"key":"2117_CR161","unstructured":"Lai, C.-H., Zou, D., & Lerman, G. (2020). Robust subspace recovery layer for unsupervised anomaly detection. In ICLR."},{"key":"2117_CR162","unstructured":"Lakshminarayanan, B., Pritzel, A., & Blundell, C. (2017). Simple and scalable predictive uncertainty estimation using deep ensembles. In NeurIPS."},{"key":"2117_CR163","unstructured":"LeCun, Y., & Cortes, C. (2005). The mnist database of handwritten digits."},{"key":"2117_CR164","unstructured":"Lee, K., Lee, H., Lee, K., & Shin, J. (2018a). Training confidence-calibrated classifiers for detecting out-of-distribution samples."},{"key":"2117_CR165","unstructured":"Lee, K., Lee, K., Lee, H., & Shin, J. (2018b). A simple unified framework for detecting out-of-distribution samples and adversarial attacks. In NeurIPS."},{"key":"2117_CR166","doi-asserted-by":"crossref","unstructured":"Lee, K., Lee, K., Min, K., Zhang, Y., Shin, J., & Lee, H. (2018c). Hierarchical novelty detection for visual object recognition. In CVPR.","DOI":"10.1109\/CVPR.2018.00114"},{"key":"2117_CR167","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.jesp.2017.09.011","volume":"74","author":"C Leys","year":"2018","unstructured":"Leys, C., Klein, O., Dominicy, Y., & Ley, C. (2018). Detecting multivariate outliers: Use a robust variant of the Mahalanobis distance. Journal of Experimental Social Psychology, 74, 150\u2013156.","journal-title":"Journal of Experimental Social Psychology"},{"issue":"4","key":"2117_CR168","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1016\/j.jesp.2013.03.013","volume":"49","author":"C Leys","year":"2013","unstructured":"Leys, C., Ley, C., Klein, O., Bernard, P., & Licata, L. (2013). Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median. Journal of Experimental Social Psychology, 49(4), 764\u2013766.","journal-title":"Journal of Experimental Social Psychology"},{"key":"2117_CR169","doi-asserted-by":"crossref","unstructured":"Li, A., Miao, Z., Cen, Y., & Cen, Y. (2017a). Anomaly detection using sparse reconstruction in crowded scenes. Multimedia Tools and Applications, 76, 26249\u201326271.","DOI":"10.1007\/s11042-016-4115-6"},{"key":"2117_CR170","unstructured":"Li, B., Zhang, Y., Chen, L., Wang, J., Yang, J., & Liu, Z. (2023a). Otter: A multi-modal model with in-context instruction tuning, arXiv preprintarXiv:2305.03726"},{"key":"2117_CR171","doi-asserted-by":"crossref","unstructured":"Li, D., Yang, Y., Song, Y.-Z., & Hospedales, T. M. (2017b). Deeper, broader and artier domain generalization. In ICCV.","DOI":"10.1109\/ICCV.2017.591"},{"key":"2117_CR172","doi-asserted-by":"crossref","unstructured":"Li, J., Chen, P., Yu, S., He, Z., Liu, S., & Jia, J. (2023b). Rethinking out-of-distribution (ood) detection: Masked image modeling is all you need. In CVPR.","DOI":"10.1109\/CVPR52729.2023.01114"},{"key":"2117_CR173","unstructured":"Li, J., Xiong, C., & Hoi, S. C. (2021). Mopro: Webly supervised learning with momentum prototypes. In ICLR."},{"key":"2117_CR174","doi-asserted-by":"crossref","unstructured":"Li, L.-J., & Fei-Fei, L. (2010). Optimol: Automatic online picture collection via incremental model learning. In IJCV.","DOI":"10.1007\/s11263-009-0265-6"},{"key":"2117_CR175","doi-asserted-by":"crossref","unstructured":"Li, Y., & Vasconcelos, N. (2020). Background data resampling for outlier-aware classification. In CVPR.","DOI":"10.1109\/CVPR42600.2020.01323"},{"key":"2117_CR176","doi-asserted-by":"crossref","unstructured":"Li, Y., Yang, J., Song, Y., Cao, L., Luo, J., & Li, L.-J. (2017). Learning from noisy labels with distillation. In CVPR.","DOI":"10.1109\/ICCV.2017.211"},{"key":"2117_CR177","unstructured":"Liang, S., Li, Y., & Srikant, R. (2018). Enhancing the reliability of out-of-distribution image detection in neural networks. In ICLR."},{"key":"2117_CR178","doi-asserted-by":"crossref","unstructured":"Lin, Z., Roy, S.D., & Li, Y. (2021). Mood: Multi-level out-of-distribution detection. In CVPR.","DOI":"10.1109\/CVPR46437.2021.01506"},{"key":"2117_CR179","unstructured":"Linderman, R., Zhang, J., Inkawhich, N., Li, H., & Chen, Y. (2023). Fine-grain inference on out-of-distribution data with hierarchical classification. In S. Chandar, R. Pascanu, H. Sedghi, & D. Precup (Eds.) Proceedings of the 2nd conference on lifelong learning agents (vol. 232 of Proceedings of Machine Learning Research, pp. 162\u2013183). PMLR."},{"key":"2117_CR180","doi-asserted-by":"crossref","unstructured":"Liu, B., Kang, H., Li, H., Hua, G., & Vasconcelos, N. (2020a). Few-shot open-set recognition using meta-learning. In CVPR.","DOI":"10.1109\/CVPR42600.2020.00882"},{"key":"2117_CR181","doi-asserted-by":"crossref","unstructured":"Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation forest. In ICDM.","DOI":"10.1109\/ICDM.2008.17"},{"key":"2117_CR182","unstructured":"Liu, H., Li, C., Wu, Q., & Lee, Y. J. (2023). Visual instruction tuning, arXiv preprint arXiv:2304.08485"},{"key":"2117_CR183","doi-asserted-by":"crossref","unstructured":"Liu, H., Li, X., Zhou, W., Chen, Y., He, Y., Xue, H., Zhang, W., & Yu, N. (2021). Spatial-phase shallow learning: Rethinking face forgery detection in frequency domain. In CVPR.","DOI":"10.1109\/CVPR46437.2021.00083"},{"key":"2117_CR184","doi-asserted-by":"crossref","first-page":"1635","DOI":"10.1016\/j.compchemeng.2004.01.009","volume":"28","author":"H Liu","year":"2004","unstructured":"Liu, H., Shah, S., & Jiang, W. (2004). On-line outlier detection and data cleaning. Computers & Chemical Engineering, 28, 1635\u20131647.","journal-title":"Computers & Chemical Engineering"},{"key":"2117_CR185","doi-asserted-by":"crossref","unstructured":"Liu, J., Lian, Z., Wang, Y., & Xiao, J. (2017). Incremental kernel null space discriminant analysis for novelty detection. In CVPR.","DOI":"10.1109\/CVPR.2017.439"},{"key":"2117_CR186","unstructured":"Liu, S., Garrepalli, R., Dietterich, T., Fern, A., & Hendrycks, D. (2018a). Open category detection with pac guarantees. In ICML."},{"key":"2117_CR187","unstructured":"Liu, W., He, J., & Chang, S.-F. (2010). Large graph construction for scalable semi-supervised learning. In ICML."},{"key":"2117_CR188","doi-asserted-by":"crossref","unstructured":"Liu, W., Luo, W., Lian, D., & Gao, S. (2018b). Future frame prediction for anomaly detection\u2014A new baseline. In CVPR.","DOI":"10.1109\/CVPR.2018.00684"},{"key":"2117_CR189","unstructured":"Liu, W., Wang, X., Owens, J. D., & Li, Y. (2020b). Energy-based out-of-distribution detection. In NeurIPS."},{"key":"2117_CR190","doi-asserted-by":"crossref","unstructured":"Liu, X., Lochman, Y., & Zach, C. (2023). Gen: Pushing the limits of softmax-based out-of-distribution detection. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 23946\u201323955).","DOI":"10.1109\/CVPR52729.2023.02293"},{"key":"2117_CR191","doi-asserted-by":"crossref","unstructured":"Liu, Z., Miao, Z., Pan, X., Zhan, X., Lin, D., Yu, S. X., & Gong, B. (2020c). Open compound domain adaptation. In CVPR.","DOI":"10.1109\/CVPR42600.2020.01242"},{"key":"2117_CR192","doi-asserted-by":"crossref","unstructured":"Liu, Z., Miao, Z., Zhan, X., Wang, J., Gong, B., & Yu, S. X. (2019). Large-scale long-tailed recognition in an open world. In CVPR.","DOI":"10.1109\/CVPR.2019.00264"},{"key":"2117_CR193","unstructured":"Loureiro, A., Torgo, L., & Soares, C. (2004). Outlier detection using clustering methods: A data cleaning application. In Proceedings of KDNet symposium on knowledge-based systems."},{"key":"2117_CR194","doi-asserted-by":"crossref","unstructured":"Lu, F., Zhu, K., Zhai, W., Zheng, K., & Cao, Y. (2023). Uncertainty-aware optimal transport for semantically coherent out-of-distribution detection. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 3282\u20133291).","DOI":"10.1109\/CVPR52729.2023.00320"},{"key":"2117_CR195","unstructured":"Lu, F., Zhu, K., Zheng, K., Zhai, W., & Cao, Y. (2023). Likelihood-aware semantic alignment for full-spectrum out-of-distribution detection, arXiv preprint arXiv:2312.01732"},{"key":"2117_CR196","unstructured":"Mackay, D. J. C. (1992). Bayesian methods for adaptive models. PhD thesis, California Institute of Technology."},{"key":"2117_CR197","first-page":"13153","volume":"32","author":"WJ Maddox","year":"2019","unstructured":"Maddox, W. J., Izmailov, P., Garipov, T., Vetrov, D. P., & Wilson, A. G. (2019). A simple baseline for Bayesian uncertainty in deep learning. Advances in Neural Information Processing Systems, 32, 13153\u201313164.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2117_CR198","unstructured":"Madry, A., Makelov, A., Schmidt, L., Tsipras, D., & Vladu, A. (2018). Towards deep learning models resistant to adversarial attacks, ICLR."},{"key":"2117_CR199","doi-asserted-by":"crossref","unstructured":"Mahdavi, A., & Carvalho, M. (2021). A survey on open set recognition, arXiv preprint arXiv:2109.00893","DOI":"10.1109\/AIKE52691.2021.00013"},{"key":"2117_CR200","unstructured":"Malinin, A., & Gales, M. (2018). Predictive uncertainty estimation via prior networks. In NeurIPS."},{"key":"2117_CR201","unstructured":"Malinin, A., & Gales, M. (2019). Reverse kl-divergence training of prior networks: Improved uncertainty and adversarial robustness. In NeurIPS."},{"key":"2117_CR202","doi-asserted-by":"crossref","unstructured":"Markou, M., & Singh, S. (2003a). Novelty detection: A review-part 1: Statistical approaches. Signal Processing, 83, 2481\u201397.","DOI":"10.1016\/j.sigpro.2003.07.018"},{"key":"2117_CR203","doi-asserted-by":"crossref","unstructured":"Markou, M., & Singh, S. (2003b). Novelty detection: A review-part 2: Neural network based approaches. Signal Processing, 83, 2499\u20132521.","DOI":"10.1016\/j.sigpro.2003.07.019"},{"key":"2117_CR204","unstructured":"Masana, M., Ruiz, I., Serrat, J., van de Weijer, J., & Lopez, A. M. (2018). Metric learning for novelty and anomaly detection. In BMVC."},{"key":"2117_CR205","unstructured":"Meinke, A., & Hein, M. (2019). Towards neural networks that provably know when they don\u2019t know, arXiv preprint arXiv:1909.12180"},{"key":"2117_CR206","unstructured":"Miljkovi\u0107, D. (2010). Review of novelty detection methods. In MIPRO."},{"key":"2117_CR207","unstructured":"Ming, Y., Cai, Z., Gu, J., Sun, Y., Li, W., & Li, Y. (2022a). Delving into out-of-distribution detection with vision-language representations. Advances in Neural Information Processing Systems, 35, 35087\u201335102."},{"key":"2117_CR208","unstructured":"Ming, Y., Fan, Y., & Li, Y. (2022b). Poem: Out-of-distribution detection with posterior sampling. In ICML."},{"key":"2117_CR209","doi-asserted-by":"crossref","unstructured":"Ming, Y., & Li, Y. (2023). How does fine-tuning impact out-of-distribution detection for vision-language models? In IJCV.","DOI":"10.1007\/s11263-023-01895-7"},{"key":"2117_CR210","unstructured":"Ming, Y., Sun, Y., Dia, O., & Li, Y. (2023). Cider: Exploiting hyperspherical embeddings for out-of-distribution detection. In ICLR."},{"key":"2117_CR211","doi-asserted-by":"crossref","unstructured":"Ming, Y., Yin, H., & Li, Y. (2022c). On the impact of spurious correlation for out-of-distribution detection. In AAAI.","DOI":"10.1609\/aaai.v36i9.21244"},{"key":"2117_CR212","doi-asserted-by":"crossref","unstructured":"Mingqiang, Z., Hui, H., & Qian, W. (2012). A graph-based clustering algorithm for anomaly intrusion detection. In International conference on Computer Science & Education (ICCSE).","DOI":"10.1109\/ICCSE.2012.6295306"},{"key":"2117_CR213","unstructured":"Miyai, A., Yang, J., Zhang, J., Ming, Y., Yu, Q., Irie, G., Li, Y., Li, H., Liu, Z., & Aizawa, K. (2024). Unsolvable problem detection: Evaluating trustworthiness of vision language models. arXiv preprint, arXiv:2403.20331"},{"key":"2117_CR214","unstructured":"Miyai, A., Yu, Q., Irie, G., & Aizawa, K. (2023a). Can pre-trained networks detect familiar out-of-distribution data? arXiv preprint arXiv:2310.00847"},{"key":"2117_CR215","unstructured":"Miyai, A., Yu, Q., Irie, G., & Aizawa, K. (2023b). Locoop: Few-shot out-of-distribution detection via prompt learning, arXiv preprint arXiv:2306.01293"},{"issue":"4","key":"2117_CR216","first-page":"631","volume":"24","author":"X Mo","year":"2013","unstructured":"Mo, X., Monga, V., Bala, R., & Fan, Z. (2013). Adaptive sparse representations for video anomaly detection. IEEE Transactions on Circuits and Systems for Video Technology, 24(4), 631\u201345.","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"2117_CR217","doi-asserted-by":"crossref","unstructured":"Mohseni, S., Pitale, M., Yadawa, J., & Wang, Z. (2020). Self-supervised learning for generalizable out-of-distribution detection. In AAAI.","DOI":"10.1609\/aaai.v34i04.5966"},{"key":"2117_CR218","unstructured":"Mohseni, S., Wang, H., Yu, Z., Xiao, C., Wang, Z., & Yadawa, J. (2021). Practical machine learning safety: A survey and primer. arXiv preprint, arXiv:2106.04823"},{"key":"2117_CR219","doi-asserted-by":"crossref","unstructured":"Morteza, P., & Li, Y. (2022). Provable guarantees for understanding out-of-distribution detection. In AAAI.","DOI":"10.1609\/aaai.v36i7.20752"},{"key":"2117_CR220","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1023\/A:1025832930864","volume":"22","author":"F Muhlenbach","year":"2004","unstructured":"Muhlenbach, F., Lallich, S., & Zighed, D. A. (2004). Identifying and handling mislabelled instances. Journal of Intelligent Information Systems, 22, 89\u2013109.","journal-title":"Journal of Intelligent Information Systems"},{"key":"2117_CR221","unstructured":"M\u00fcnz, G., Li, S., & Carle, G. (2007). Traffic anomaly detection using k-means clustering. In GI\/ITG workshop MMBnet."},{"key":"2117_CR222","unstructured":"Nalisnick, E., Matsukawa, A., Teh, Y. W., Gorur, D., & Lakshminarayanan, B. (2018). Do deep generative models know what they don\u2019t know? In NeurIPS."},{"key":"2117_CR223","unstructured":"Nandy, J., Hsu, W., & Lee, M. L. (2020). Towards maximizing the representation gap between in-domain & out-of-distribution examples. In NeurIPS."},{"key":"2117_CR224","doi-asserted-by":"crossref","unstructured":"Neal, L., Olson, M., Fern, X., Wong, W.-K., & Li, F. (2018). Open set learning with counterfactual images. In ECCV.","DOI":"10.1007\/978-3-030-01231-1_38"},{"key":"2117_CR225","unstructured":"Neal, R. M. (2012). Bayesian learning for neural networks."},{"key":"2117_CR226","unstructured":"Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., & Ng, A. Y. (2011). Reading digits in natural images with unsupervised feature learning."},{"key":"2117_CR227","unstructured":"Ngiam, J., Chen, Z., Koh, P. W., & Ng, A. Y. (2011). Learning deep energy models. In ICML."},{"key":"2117_CR228","doi-asserted-by":"crossref","unstructured":"Nguyen, A., Yosinski, J., & Clune, J. (2015). Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. In CVPR.","DOI":"10.1109\/CVPR.2015.7298640"},{"key":"2117_CR229","unstructured":"Nguyen, D. T., Lou, Z., Klar, M., & Brox, T. (2019). Anomaly detection with multiple-hypotheses predictions. In ICML."},{"key":"2117_CR230","unstructured":"Nguyen, D. T., Mummadi, C. K., Ngo, T. P. N., Nguyen, T. H. P., Beggel, L., & Brox, T. (2020). Self: Learning to filter noisy labels with self-ensembling. In ICLR."},{"key":"2117_CR231","unstructured":"Nguyen, V. D. (2022). Out-of-distribution detection for lidar-based 3d object detection, Master\u2019s thesis, University of Waterloo."},{"key":"2117_CR232","doi-asserted-by":"crossref","unstructured":"Nie, J., Zhang, Y., Fang, Z., Liu, T., Han, B., & Tian, X. (2023). Out-of-distribution detection with negative prompts. In The twelfth international conference on learning representations.","DOI":"10.1007\/s11263-024-02210-8"},{"key":"2117_CR233","doi-asserted-by":"crossref","unstructured":"Nixon, K. A., Aimale, V., & Rowe, R. K. (2008). Spoof detection schemes. In Handbook of biometrics.","DOI":"10.1007\/978-0-387-71041-9_20"},{"key":"2117_CR234","doi-asserted-by":"crossref","unstructured":"Noble, C. C., & Cook, D. J. (2003). Graph-based anomaly detection. In SIGKDD.","DOI":"10.1145\/956750.956831"},{"key":"2117_CR235","doi-asserted-by":"crossref","unstructured":"Orair, G. H., Teixeira, C. H., Meira, W., Jr., Wang, Y., & Parthasarathy, S. (2010). Distance-based outlier detection: consolidation and renewed bearing. In Proceedings of the VLDB endowment.","DOI":"10.14778\/1920841.1921021"},{"key":"2117_CR236","unstructured":"Osawa, K., Swaroop, S., Jain, A., Eschenhagen, R., Turner, R. E., Yokota, R., & Khan, M. E. (2019). Practical deep learning with Bayesian principles. In NeurIPS."},{"key":"2117_CR237","doi-asserted-by":"crossref","unstructured":"Oza, P., & Patel, V. M. (2019). C2ae: Class conditioned auto-encoder for open-set recognition. In CVPR.","DOI":"10.1109\/CVPR.2019.00241"},{"key":"2117_CR238","doi-asserted-by":"crossref","unstructured":"Panareda Busto, P., & Gall, J. (2017). Open set domain adaptation. In ICCV.","DOI":"10.1109\/ICCV.2017.88"},{"key":"2117_CR239","unstructured":"Pang, G., Shen, C., Cao, L., & Hengel, A. V. D. (2020). Deep learning for anomaly detection: A review, arXiv preprint arXiv:2007.02500"},{"key":"2117_CR240","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.neucom.2021.02.007","volume":"441","author":"A-A Papadopoulos","year":"2021","unstructured":"Papadopoulos, A.-A., Rajati, M. R., Shaikh, N., & Wang, J. (2021). Outlier exposure with confidence control for out-of-distribution detection. Neurocomputing, 441, 138\u2013150.","journal-title":"Neurocomputing"},{"key":"2117_CR241","doi-asserted-by":"crossref","unstructured":"Park, H., Noh, J., & Ham, B. (2020). Learning memory-guided normality for anomaly detection. In CVPR.","DOI":"10.1109\/CVPR42600.2020.01438"},{"key":"2117_CR242","doi-asserted-by":"crossref","unstructured":"Park, J., Chai, J. C. L., Yoon, J., & Teoh, A. B. J. (2023a). Understanding the feature norm for out-of-distribution detection. In Proceedings of the IEEE\/CVF international conference on computer vision (pp. 1557\u20131567).","DOI":"10.1109\/ICCV51070.2023.00150"},{"key":"2117_CR243","doi-asserted-by":"crossref","unstructured":"Park, J., Jung, Y. G., & Teoh, A. B. J. (2023b). Nearest neighbor guidance for out-of-distribution detection. In Proceedings of the IEEE\/CVF international conference on computer vision (pp. 1686\u20131695).","DOI":"10.1109\/ICCV51070.2023.00162"},{"issue":"10","key":"2117_CR244","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3561381","volume":"55","author":"J Parmar","year":"2023","unstructured":"Parmar, J., Chouhan, S., Raychoudhury, V., & Rathore, S. (2023). Open-world machine learning: Applications, challenges, and opportunities. ACM Computing Surveys, 55(10), 1\u201337.","journal-title":"ACM Computing Surveys"},{"key":"2117_CR245","doi-asserted-by":"crossref","first-page":"1065","DOI":"10.1214\/aoms\/1177704472","volume":"33","author":"E Parzen","year":"1962","unstructured":"Parzen, E. (1962). On estimation of a probability density function and mode. The Annals of Mathematical Statistics, 33, 1065\u20131076.","journal-title":"The Annals of Mathematical Statistics"},{"key":"2117_CR246","doi-asserted-by":"crossref","first-page":"2268","DOI":"10.1109\/TIFS.2016.2578288","volume":"11","author":"K Patel","year":"2016","unstructured":"Patel, K., Han, H., & Jain, A. K. (2016). Secure face unlock: Spoof detection on smartphones. IEEE Transactions on Information Forensics and Security, 11, 2268\u20132283.","journal-title":"IEEE Transactions on Information Forensics and Security"},{"key":"2117_CR247","doi-asserted-by":"crossref","unstructured":"Pathak, D., Agrawal, P., Efros, A. A., & Darrell, T. (2017). Curiosity-driven exploration by self-supervised prediction. In ICML.","DOI":"10.1109\/CVPRW.2017.70"},{"key":"2117_CR248","doi-asserted-by":"crossref","unstructured":"Perera, P., Morariu, V. I., Jain, R., Manjunatha, V., Wigington, C., Ordonez, V., & Patel, V. M. (2020). Generative-discriminative feature representations for open-set recognition. In CVPR.","DOI":"10.1109\/CVPR42600.2020.01183"},{"key":"2117_CR249","doi-asserted-by":"crossref","unstructured":"Perera, P., Nallapati, R., & Xiang, B. (2019). Ocgan: One-class novelty detection using gans with constrained latent representations. In CVPR.","DOI":"10.1109\/CVPR.2019.00301"},{"key":"2117_CR250","doi-asserted-by":"crossref","unstructured":"Perera, P., & Patel, V. M. (2019). Deep transfer learning for multiple class novelty detection. In CVPR.","DOI":"10.1109\/CVPR.2019.01181"},{"key":"2117_CR251","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1016\/0893-6080(89)90045-2","volume":"2","author":"C Peterson","year":"1989","unstructured":"Peterson, C., & Hartman, E. (1989). Explorations of the mean field theory learning algorithm. Neural Networks, 2, 475\u2013494.","journal-title":"Neural Networks"},{"key":"2117_CR252","unstructured":"Pidhorskyi, S., Almohsen, R., Adjeroh, D. A., & Doretto, G. (2018). Generative probabilistic novelty detection with adversarial autoencoders. In NeurIPS."},{"key":"2117_CR253","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.sigpro.2013.12.026","volume":"99","author":"MA Pimentel","year":"2014","unstructured":"Pimentel, M. A., Clifton, D. A., Clifton, L., & Tarassenko, L. (2014). A review of novelty detection. Signal Processing, 99, 215\u2013249.","journal-title":"Signal Processing"},{"key":"2117_CR254","unstructured":"Pleiss, G., Souza, A., Kim, J., Li, B., & Weinberger, K. Q. (2019). Neural network out-of-distribution detection for regression tasks."},{"key":"2117_CR255","doi-asserted-by":"crossref","unstructured":"Polatkan, G., Jafarpour, S., Brasoveanu, A., Hughes, S., & Daubechies, I. (2009). Detection of forgery in paintings using supervised learning. In ICIP.","DOI":"10.1109\/ICIP.2009.5413338"},{"key":"2117_CR256","unstructured":"Powers, D. M. (2020). Evaluation: From precision, recall and f-measure to roc, informedness, markedness and correlation. In JMLT."},{"key":"2117_CR257","doi-asserted-by":"crossref","unstructured":"Qui nonero-Candela, J., Sugiyama, M., Lawrence, N. D., & Schwaighofer, A. (2009). Dataset shift in machine learning. MIT Press.","DOI":"10.7551\/mitpress\/9780262170055.001.0001"},{"key":"2117_CR258","unstructured":"Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., & Krueger, G. (2021). Learning transferable visual models from natural language supervision. In ICML."},{"issue":"2","key":"2117_CR259","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1137\/1026034","volume":"26","author":"RA Redner","year":"1984","unstructured":"Redner, R. A., & Walker, H. F. (1984). Mixture densities, maximum likelihood and the em algorithm. SIAM Review, 26(2), 195\u2013239.","journal-title":"SIAM Review"},{"key":"2117_CR260","unstructured":"Ren, J., Fort, S., Liu, J., Roy, A. G., Padhy, S., & Lakshminarayanan, B. (2021). A simple fix to Mahalanobis distance for improving near-ood detection, arXiv preprint arXiv:2106.09022"},{"key":"2117_CR261","unstructured":"Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M. A., Dillon, J. V., & Lakshminarayanan, B. (2019). Likelihood ratios for out-of-distribution detection. In NeurIPS."},{"key":"2117_CR262","unstructured":"Rezende, D., & Mohamed, S. (2015). Variational inference with normalizing flows. In ICML."},{"issue":"3","key":"2117_CR263","doi-asserted-by":"crossref","first-page":"762","DOI":"10.1109\/TPAMI.2017.2707495","volume":"40","author":"EM Rudd","year":"2017","unstructured":"Rudd, E. M., Jain, L. P., Scheirer, W. J., & Boult, T. E. (2017). The extreme value machine. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(3), 762\u2013768.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2117_CR264","doi-asserted-by":"crossref","unstructured":"Ruff, L., Kauffmann, J. R., Vandermeulen, R. A., Montavon, G., Samek, W., Kloft, M., Dietterich, T. G., & M\u00fcller, K.-R. (2021). A unifying review of deep and shallow anomaly detection. In Proceedings of the IEEE.","DOI":"10.1109\/JPROC.2021.3052449"},{"key":"2117_CR265","unstructured":"Ruff, L., Vandermeulen, R., Goernitz, N., Deecke, L., Siddiqui, S. A., Binder, A., M\u00fcller, E., & Kloft, M. (2018). Deep one-class classification. In ICML."},{"key":"2117_CR266","unstructured":"Ruff, L., Vandermeulen, R. A., G\u00f6rnitz, N., Binder, A., M\u00fcller, K.-R. M\u00fcller, E., & Kloft, M. (2020). Deep semi-supervised anomaly detection. In ICLR."},{"key":"2117_CR267","doi-asserted-by":"crossref","unstructured":"Sabokrou, M., Khalooei, M., Fathy, M., & Adeli, E. (2018). Adversarially learned one-class classifier for novelty detection. In CVPR.","DOI":"10.1109\/CVPR.2018.00356"},{"key":"2117_CR268","unstructured":"Salehi, M., Mirzaei, H., Hendrycks, D., Li, Y., Rohban, M. H., & Sabokrou, M. (2021). A unified survey on anomaly, novelty, open-set, and out-of-distribution detection: Solutions and future challenges, arXiv preprint arXiv:2110.14051"},{"key":"2117_CR269","unstructured":"Sastry, C. S., & Oore, S. (2019). Detecting out-of-distribution examples with in-distribution examples and gram matrices. In NeurIPS-W."},{"key":"2117_CR270","unstructured":"Sastry, C. S., & Oore, S. (2020). Detecting out-of-distribution examples with gram matrices. In ICML."},{"key":"2117_CR271","doi-asserted-by":"crossref","unstructured":"Scheirer, W. J., de Rezende Rocha, A., Sapkota, A., & Boult, T. E. (2013). Toward open set recognition. In TPAMI.","DOI":"10.1109\/TPAMI.2012.256"},{"key":"2117_CR272","doi-asserted-by":"crossref","unstructured":"Scheirer, W. J., Jain, L. P., & Boult, T. E. (2014). Probability models for open set recognition. In TPAMI.","DOI":"10.1109\/TPAMI.2014.2321392"},{"key":"2117_CR273","doi-asserted-by":"crossref","unstructured":"Schlachter, P., Liao, Y., & Yang, B. (2019). Open-set recognition using intra-class splitting. In EUSIPCO.","DOI":"10.1007\/s42979-020-0086-9"},{"key":"2117_CR274","doi-asserted-by":"crossref","unstructured":"Sedlmeier, A., Gabor, T., Phan, T., Belzner, L., & Linnhoff-Popien, C. (2019). Uncertainty-based out-of-distribution detection in deep reinforcement learning, arXiv preprint arXiv:1901.02219","DOI":"10.5220\/0008949905220529"},{"key":"2117_CR275","unstructured":"Serr\u00e0, J., \u00c1lvarez, D., G\u00f3mez, V., Slizovskaia, O., N\u00fa nez, J. F., & Luque, J. (2020). Input complexity and out-of-distribution detection with likelihood-based generative models."},{"key":"2117_CR276","unstructured":"Shafaei, A., Schmidt, M., & Little, J. J. (2019). A less biased evaluation of out-of-distribution sample detectors. In BMVC."},{"issue":"3","key":"2117_CR277","first-page":"66","volume":"9","author":"G Shafer","year":"2008","unstructured":"Shafer, G., & Vovk, V. (2008). A tutorial on conformal prediction. Journal of Machine Learning Research, 9(3), 66.","journal-title":"Journal of Machine Learning Research"},{"key":"2117_CR278","unstructured":"Shalev, G., Adi, Y., & Keshet, J. (2018). Out-of-distribution detection using multiple semantic label representations. In NeurIPS."},{"key":"2117_CR279","doi-asserted-by":"crossref","unstructured":"Shalev, G., Shalev, G.-L., & Keshet, J. (2022). A baseline for detecting out-of-distribution examples in image captioning. arXiv preprint, arXiv:2207.05418","DOI":"10.1145\/3503161.3548340"},{"key":"2117_CR280","doi-asserted-by":"crossref","unstructured":"Shao, R., Perera, P., Yuen, P. C., & Patel, V. M. (2020). Open-set adversarial defense. In ECCV.","DOI":"10.1007\/978-3-030-58520-4_40"},{"key":"2117_CR281","doi-asserted-by":"crossref","unstructured":"Shu, Y., Cao, Z., Wang, C., Wang, J., & Long, M. (2021). Open domain generalization with domain-augmented meta-learning. In CVPR.","DOI":"10.1109\/CVPR46437.2021.00950"},{"key":"2117_CR282","doi-asserted-by":"crossref","first-page":"7146","DOI":"10.1038\/s41598-020-63649-6","volume":"10","author":"Y Shu","year":"2020","unstructured":"Shu, Y., Shi, Y., Wang, Y., Huang, T., & Tian, Y. (2020). p-odn: Prototype-based open deep network for open set recognition. Scientific Reports, 10, 7146.","journal-title":"Scientific Reports"},{"key":"2117_CR283","first-page":"18","volume":"7","author":"RL Smith","year":"1990","unstructured":"Smith, R. L. (1990). Extreme value theory. Handbook of Applicable Mathematics, 7, 18.","journal-title":"Handbook of Applicable Mathematics"},{"key":"2117_CR284","doi-asserted-by":"crossref","unstructured":"Sorio, E., Bartoli, A., Davanzo, G., & Medvet, E. (2010). Open world classification of printed invoices. In Proceedings of the 10th ACM symposium on document engineering.","DOI":"10.1145\/1860559.1860599"},{"key":"2117_CR285","unstructured":"Sricharan, K., & Srivastava, A. (2018). Building robust classifiers through generation of confident out of distribution examples. In NeurIPS-W."},{"key":"2117_CR286","unstructured":"Sugiyama, M., & Borgwardt, K. (2013). Rapid distance-based outlier detection via sampling. In NIPS."},{"key":"2117_CR287","unstructured":"Sun, X., Ding, H., Zhang, C., Lin, G., & Ling, K.-V. (2021a). M2iosr: Maximal mutual information open set recognition, arXiv preprint arXiv:2108.02373"},{"key":"2117_CR288","doi-asserted-by":"crossref","unstructured":"Sun, X., Yang, Z., Zhang, C., Ling, K.-V., & Peng, G. (2020). Conditional Gaussian distribution learning for open set recognition. In CVPR.","DOI":"10.1109\/CVPR42600.2020.01349"},{"key":"2117_CR289","unstructured":"Sun, Y., Guo, C., & Li, Y. (2021b). React: Out-of-distribution detection with rectified activations. In NeurIPS."},{"key":"2117_CR290","doi-asserted-by":"crossref","unstructured":"Sun, Y., & Li, Y. (2022). Dice: Leveraging sparsification for out-of-distribution detection. In ECCV.","DOI":"10.1007\/978-3-031-20053-3_40"},{"key":"2117_CR291","unstructured":"Sun, Y., Ming, Y., Zhu, X., & Li, Y. (2022). Out-of-distribution detection with deep nearest neighbors. In ICML."},{"key":"2117_CR292","doi-asserted-by":"crossref","unstructured":"Syarif, I., Prugel-Bennett, A., & Wills, G. (2012). Unsupervised clustering approach for network anomaly detection. In International conference on networked digital technologies.","DOI":"10.1007\/978-3-642-30507-8_13"},{"key":"2117_CR293","unstructured":"Tack, J., Mo, S., Jeong, J., & Shin, J. (2020). Csi: Novelty detection via contrastive learning on distributionally shifted instances. In NeurIPS."},{"key":"2117_CR294","unstructured":"Tao, L., Du, X., Zhu, X., & Li, Y. (2023). Non-parametric outlier synthesis. In ICLR."},{"key":"2117_CR295","first-page":"1","volume":"2020","author":"MI Tariq","year":"2020","unstructured":"Tariq, M. I., Memon, N. A., Ahmed, S., Tayyaba, S., Mushtaq, M. T., Mian, N. A., Imran, M., & Ashraf, M. W. (2020). A review of deep learning security and privacy defensive techniques. Mobile Information Systems, 2020, 1\u20138.","journal-title":"Mobile Information Systems"},{"key":"2117_CR296","unstructured":"Tax, D. M. J. (2002). One-class classification: Concept learning in the absence of counter-examples."},{"key":"2117_CR297","doi-asserted-by":"crossref","unstructured":"Techapanurak, E., Suganuma, M., & Okatani, T. (2020). Hyperparameter-free out-of-distribution detection using cosine similarity. In ACCV.","DOI":"10.1007\/978-3-030-69538-5_4"},{"key":"2117_CR298","doi-asserted-by":"crossref","unstructured":"Thulasidasan, S., Chennupati, G., Bilmes, J., Bhattacharya, T., & Michalak, S. (2019). On mixup training: Improved calibration and predictive uncertainty for deep neural networks. In NeurIPS.","DOI":"10.2172\/1525811"},{"key":"2117_CR299","doi-asserted-by":"crossref","unstructured":"Thulasidasan, S., Thapa, S., Dhaubhadel, S., Chennupati, G., Bhattacharya, T., & Bilmes, J. (2021). An effective baseline for robustness to distributional shift, arXiv preprint arXiv:2105.07107","DOI":"10.1109\/ICMLA52953.2021.00050"},{"key":"2117_CR300","doi-asserted-by":"crossref","unstructured":"Tian, J., Azarian, M. H., & Pecht, M. (2014). Anomaly detection using self-organizing maps-based k-nearest neighbor algorithm. In PHM society European conference.","DOI":"10.36001\/phme.2014.v2i1.1554"},{"key":"2117_CR301","doi-asserted-by":"crossref","unstructured":"Tian, K., Zhou, S., Fan, J., & Guan, J. (2019). Learning competitive and discriminative reconstructions for anomaly detection. In AAAI.","DOI":"10.1609\/aaai.v33i01.33015167"},{"key":"2117_CR302","doi-asserted-by":"crossref","unstructured":"Torralba, A., Fergus, R., & Freeman, W. T. (2008). 80 million tiny images: A large data set for nonparametric object and scene recognition. In TPAMI.","DOI":"10.1109\/TPAMI.2008.128"},{"key":"2117_CR303","doi-asserted-by":"crossref","unstructured":"Turcotte, M., Moore, J., Heard, N., & McPhall, A. (2016). Poisson factorization for peer-based anomaly detection. In IEEE conference on intelligence and security informatics (ISI).","DOI":"10.1109\/ISI.2016.7745472"},{"key":"2117_CR304","unstructured":"Van Amersfoort, J., Smith, L., Teh, Y. W., & Gal, Y. (2020). Uncertainty estimation using a single deep deterministic neural network. In ICML."},{"key":"2117_CR305","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1371\/journal.pmed.0020267","volume":"2","author":"J Van den Broeck","year":"2005","unstructured":"Van den Broeck, J., Argeseanu Cunningham, S., Eeckels, R., & Herbst, K. (2005). Data cleaning: Detecting, diagnosing, and editing data abnormalities. PLoS Medicine, 2, 267.","journal-title":"PLoS Medicine"},{"key":"2117_CR306","unstructured":"Van Oord, A., Kalchbrenner, N., & Kavukcuoglu, K. (2016). Pixel recurrent neural networks. In ICML."},{"key":"2117_CR307","first-page":"493","volume":"2","author":"J Van Ryzin","year":"1973","unstructured":"Van Ryzin, J. (1973). A histogram method of density estimation. Communications in Statistics-Theory and Methods, 2, 493\u2013506.","journal-title":"Communications in Statistics-Theory and Methods"},{"key":"2117_CR308","doi-asserted-by":"crossref","unstructured":"Vaze, S., Han, K., Vedaldi, A., & Zisserman, A. (2022a). Generalized category discovery. In CVPR.","DOI":"10.1109\/CVPR52688.2022.00734"},{"key":"2117_CR309","unstructured":"Vaze, S., Han, K., Vedaldi, A., & Zisserman, A. (2022b). Open-set recognition: A good closed-set classifier is all you need. In ICLR."},{"key":"2117_CR310","unstructured":"Vernekar, S., Gaurav, A., Abdelzad, V., Denouden, T., Salay, R., & Czarnecki, K. (2019). Out-of-distribution detection in classifiers via generation. In NeurIPS-W."},{"key":"2117_CR311","unstructured":"Vinyals, O., Ewalds, T., Bartunov, S., Georgiev, P., Vezhnevets, A. S., Yeo, M., Makhzani, A., K\u00fcttler, H., Agapiou, J., Schrittwieser, J., & Quan, J. (2017). Starcraft II: A new challenge for reinforcement learning, arXiv preprint arXiv:1708.04782"},{"key":"2117_CR312","doi-asserted-by":"crossref","unstructured":"Vyas, A., Jammalamadaka, N., Zhu, X., Das, D., Kaul, B., & Willke, T. L. (2018). Out-of-distribution detection using an ensemble of self supervised leave-out classifiers. In ECCV.","DOI":"10.1007\/978-3-030-01237-3_34"},{"key":"2117_CR313","doi-asserted-by":"crossref","unstructured":"Wang, H., Bah, M. J., & Hammad, M. (2019a). Progress in outlier detection techniques: A survey. IEEE Access, 7, 107964\u2013108000.","DOI":"10.1109\/ACCESS.2019.2932769"},{"key":"2117_CR314","doi-asserted-by":"crossref","unstructured":"Wang, H., Li, Y., Yao, H., & Li, X. (2023a). Clipn for zero-shot ood detection: Teaching clip to say no. In Proceedings of the IEEE\/CVF international conference on computer vision (pp. 1802\u20131812).","DOI":"10.1109\/ICCV51070.2023.00173"},{"key":"2117_CR315","doi-asserted-by":"crossref","unstructured":"Wang, H., Li, Z., Feng, L., & Zhang, W. (2022a). Vim: Out-of-distribution with virtual-logit matching. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition.","DOI":"10.1109\/CVPR52688.2022.00487"},{"key":"2117_CR316","first-page":"29074","volume":"34","author":"H Wang","year":"2021","unstructured":"Wang, H., Liu, W., Bocchieri, A., & Li, Y. (2021). Can multi-label classification networks know what they don\u2019t know? NeurIPS, 34, 29074\u201329087.","journal-title":"NeurIPS"},{"key":"2117_CR317","doi-asserted-by":"crossref","unstructured":"Wang, H., Wu, X., Huang, Z., & Xing, E. P. (2020). High-frequency component helps explain the generalization of convolutional neural networks. In CVPR.","DOI":"10.1109\/CVPR42600.2020.00871"},{"key":"2117_CR318","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.neucom.2018.05.083","volume":"312","author":"M Wang","year":"2018","unstructured":"Wang, M., & Deng, W. (2018). Deep visual domain adaptation: A survey. Neurocomputing, 312, 135\u2013153.","journal-title":"Neurocomputing"},{"key":"2117_CR319","unstructured":"Wang, Q., Fang, Z., Zhang, Y., Liu, F., Li, Y., & Han, B. (2023b). Learning to augment distributions for out-of-distribution detection. Advances in Neural Information Processing Systems, 36, 66."},{"key":"2117_CR320","unstructured":"Wang, Q., Liu, F., Zhang, Y., Zhang, J., Gong, C., Liu, T., & Han, B. (2022b). Watermarking for out-of-distribution detection. In NeurIPS."},{"key":"2117_CR321","unstructured":"Wang, Q., Ye, J., Liu, F., Dai, Q., Kalander, M., Liu, T., Hao, J., & Han, B. (2023c). Out-of-distribution detection with implicit outlier transformation."},{"key":"2117_CR322","doi-asserted-by":"crossref","unstructured":"Wang, W., Zheng, V. W., Yu, H., & Miao, C. (2019b). A survey of zero-shot learning: Settings, methods, and applications. In TIST.","DOI":"10.1145\/3293318"},{"key":"2117_CR323","doi-asserted-by":"crossref","unstructured":"Wang, Y., Li, B., Che, T., Zhou, K., Liu, Z., & Li, D. (2021). Energy-based open-world uncertainty modeling for confidence calibration. In ICCV.","DOI":"10.1109\/ICCV48922.2021.00917"},{"key":"2117_CR324","doi-asserted-by":"crossref","unstructured":"Wang, Y., Liu, W., Ma, X., Bailey, J., Zha, H., Song, L., & Xia, S.-T. (2018). Iterative learning with open-set noisy labels. In CVPR.","DOI":"10.1109\/CVPR.2018.00906"},{"key":"2117_CR325","unstructured":"Wei, H., Xie, R., Cheng, H., Feng, L., An, B., & Li, Y. (2022). Mitigating neural network overconfidence with logit normalization. In ICML."},{"key":"2117_CR326","unstructured":"Welling, M., & Teh, Y. W. (2011). Bayesian learning via stochastic gradient Langevin dynamics. In ICML."},{"key":"2117_CR327","doi-asserted-by":"crossref","first-page":"746","DOI":"10.1109\/TIFS.2015.2400395","volume":"10","author":"D Wen","year":"2015","unstructured":"Wen, D., Han, H., & Jain, A. K. (2015). Face spoof detection with image distortion analysis. IEEE Transactions on Information Forensics and Security, 10, 746\u2013761.","journal-title":"IEEE Transactions on Information Forensics and Security"},{"key":"2117_CR328","unstructured":"Wenzel, F., Roth, K., Veeling, B. S., \u015awiatkowski, J., Tran, L., Mandt, S., Snoek, J., Salimans, T., Jenatton, R., & Nowozin, S. (2020). How good is the Bayes posterior in deep neural networks really? In ICML."},{"key":"2117_CR329","unstructured":"Wettschereck, D. (1994). A study of distance-based machine learning algorithms."},{"key":"2117_CR330","unstructured":"Wikipedia contributors. (2021). Outlier from Wikipedia, the free encyclopedia. Retrieved August 12, 2021"},{"key":"2117_CR331","doi-asserted-by":"crossref","unstructured":"Wu, X., Lu, J., Fang, Z., & Zhang, G. (2023). Meta ood learning for continuously adaptive ood detection. In Proceedings of the IEEE\/CVF international conference on computer vision (pp. 19353\u201319364).","DOI":"10.1109\/ICCV51070.2023.01773"},{"key":"2117_CR332","doi-asserted-by":"crossref","unstructured":"Wu, Z.-F., Wei, T., Jiang, J., Mao, C., Tang, M., & Li, Y.-F. (2021). Ngc: A unified framework for learning with open-world noisy data. In ICCV.","DOI":"10.1109\/ICCV48922.2021.00013"},{"key":"2117_CR333","doi-asserted-by":"crossref","unstructured":"Xia, Y., Cao, X., Wen, F., Hua, G., & Sun, J. (2015). Learning discriminative reconstructions for unsupervised outlier removal. In CVPR.","DOI":"10.1109\/ICCV.2015.177"},{"key":"2117_CR334","doi-asserted-by":"crossref","first-page":"1477","DOI":"10.1109\/LSP.2015.2410031","volume":"22","author":"T Xiao","year":"2015","unstructured":"Xiao, T., Zhang, C., & Zha, H. (2015). Learning to detect anomalies in surveillance video. IEEE Signal Processing Letters, 22, 1477\u20131481.","journal-title":"IEEE Signal Processing Letters"},{"key":"2117_CR335","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/j.patcog.2012.06.017","volume":"46","author":"Y Xiao","year":"2013","unstructured":"Xiao, Y., Wang, H., Xu, W., & Zhou, J. (2013). L1 norm based kpca for novelty detection. Pattern Recognition, 46, 389\u2013396.","journal-title":"Pattern Recognition"},{"key":"2117_CR336","unstructured":"Xiao, Z., Yan, Q., & Amit, Y. (2020). Likelihood regret: An out-of-distribution detection score for variational auto-encoder. In NeurIPS."},{"key":"2117_CR337","doi-asserted-by":"crossref","unstructured":"Xie, M., Hu, J., & Tian, B. (2012). Histogram-based online anomaly detection in hierarchical wireless sensor networks. In ICTSPCC.","DOI":"10.1109\/TrustCom.2012.173"},{"key":"2117_CR338","doi-asserted-by":"crossref","unstructured":"Xu, H., Liu, B., Shu, L., & Yu, P. (2019). Open-world learning and application to product classification. In WWW.","DOI":"10.1145\/3308558.3313644"},{"key":"2117_CR339","doi-asserted-by":"crossref","unstructured":"Yan, X., Zhang, H., Xu, X., Hu, X., & Heng, P.-A. (2021). Learning semantic context from normal samples for unsupervised anomaly detection. In AAAI.","DOI":"10.1609\/aaai.v35i4.16420"},{"key":"2117_CR340","doi-asserted-by":"crossref","unstructured":"Yang, J., Chen, W., Feng, L., Yan, X., Zheng, H., & Zhang, W. (2020a). Webly supervised image classification with metadata: Automatic noisy label correction via visual-semantic graph. In ACM multimedia.","DOI":"10.1145\/3394171.3413952"},{"key":"2117_CR341","doi-asserted-by":"crossref","unstructured":"Yang, J., Feng, L., Chen, W., Yan, X., Zheng, H., Luo, P., & Zhang, W. (2020b). Webly supervised image classification with self-contained confidence. In ECCV.","DOI":"10.1007\/978-3-030-58598-3_46"},{"key":"2117_CR342","doi-asserted-by":"crossref","unstructured":"Yang, J., Wang, H., Feng, L., Yan, X., Zheng, H., Zhang, W., & Liu, Z. (2021). Semantically coherent out-of-distribution detection. In ICCV.","DOI":"10.1109\/ICCV48922.2021.00819"},{"key":"2117_CR343","unstructured":"Yang, J., Wang, P., Zou, D., Zhou, Z., Ding, K., Peng, W., Wang, H., Chen, G., Li, B., Sun, Y., Du, X., Zhou, K., Zhang, W., Hendrycks, D., Li, Y., & Liu, Z. (2022a). Openood: Benchmarking generalized out-of-distribution detection. In NeurIPS."},{"key":"2117_CR344","unstructured":"Yang, J., Zhou, K., & Liu, Z. (2022b). Full-spectrum out-of-distribution detection, arXiv preprint arXiv:2204.05306"},{"key":"2117_CR345","doi-asserted-by":"crossref","unstructured":"Yang, P., Baracchi, D., Ni, R., Zhao, Y., Argenti, F., & Piva, A. (2020c). A survey of deep learning-based source image forensics. Journal of Imaging, 6, 66.","DOI":"10.3390\/jimaging6030009"},{"key":"2117_CR346","doi-asserted-by":"crossref","unstructured":"Yang, X., Latecki, L. J., & Pokrajac, D. (2009). Outlier detection with globally optimal exemplar-based gmm. In SIAM.","DOI":"10.1137\/1.9781611972795.13"},{"key":"2117_CR347","doi-asserted-by":"crossref","unstructured":"Yang, Y., Gao, R., & Xu, Q. (2022c). Out-of-distribution detection with semantic mismatch under masking. In ECCV.","DOI":"10.1007\/978-3-031-20053-3_22"},{"key":"2117_CR348","unstructured":"Yang, Z., Li, L., Lin, K., Wang, J., Lin, C.-C., Liu, Z., & Wang, L. (2023). The dawn of lmms Preliminary explorations with gpt-4v (ision). arXiv preprint, arXiv:2309.17421"},{"key":"2117_CR349","doi-asserted-by":"crossref","unstructured":"Yoshihashi, R., Shao, W., Kawakami, R., You, S., Iida, M., & Naemura, T. (2019). Classification-reconstruction learning for open-set recognition. In CVPR.","DOI":"10.1109\/CVPR.2019.00414"},{"key":"2117_CR350","doi-asserted-by":"crossref","unstructured":"Yu, Q., & Aizawa, K. (2019). Unsupervised out-of-distribution detection by maximum classifier discrepancy. In ICCV.","DOI":"10.1109\/ICCV.2019.00961"},{"key":"2117_CR351","doi-asserted-by":"crossref","unstructured":"Yue, Z., Wang, T., Sun, Q., Hua, X.-S., & Zhang, H. (2021). Counterfactual zero-shot and open-set visual recognition. In CVPR.","DOI":"10.1109\/CVPR46437.2021.01515"},{"key":"2117_CR352","doi-asserted-by":"crossref","unstructured":"Yun, S., Han, D., Oh, S. J., Chun, S., Choe, J., & Yoo, Y. (2019). Cutmix: Regularization strategy to train strong classifiers with localizable features. In CVPR.","DOI":"10.1109\/ICCV.2019.00612"},{"key":"2117_CR353","doi-asserted-by":"crossref","unstructured":"Zaeemzadeh, A., Bisagno, N., Sambugaro, Z., Conci, N., Rahnavard, N., & Shah, M. (2021). Out-of-distribution detection using union of 1-dimensional subspaces. In CVPR.","DOI":"10.1109\/CVPR46437.2021.00933"},{"key":"2117_CR354","unstructured":"Zenati, H., Foo, C. S., Lecouat, B., Manek, G., & Chandrasekhar, V. R. (2018). Efficient gan-based anomaly detection. In ICLR-W."},{"key":"2117_CR355","unstructured":"Zhai, S., Cheng, Y., Lu, W., & Zhang, Z. (2016). Deep structured energy based models for anomaly detection. In ICML."},{"key":"2117_CR356","doi-asserted-by":"crossref","unstructured":"Zhang, B., & Zuo, W. (2008). Learning from positive and unlabeled examples: A survey. In International symposiums on information processing.","DOI":"10.1109\/ISIP.2008.79"},{"key":"2117_CR357","doi-asserted-by":"crossref","unstructured":"Zhang, H., Li, A., Guo, J., & Guo, Y. (2020). Hybrid models for open set recognition. In ECCV.","DOI":"10.1007\/978-3-030-58580-8_7"},{"key":"2117_CR358","unstructured":"Zhang, H., & Patel, V. M. (2016). Sparse representation-based open set recognition. In TPAMI."},{"key":"2117_CR359","unstructured":"Zhang, J., Fu, Q., Chen, X., Du, L., Li, Z., Wang, G., Han, S., & Zhang, D. (2023a). Out-of-distribution detection based on in-distribution data patterns memorization with modern Hopfield energy. In ICLR."},{"key":"2117_CR360","doi-asserted-by":"crossref","unstructured":"Zhang, J., Inkawhich, N., Linderman, R., Chen, Y., & Li, H. (2023b). Mixture outlier exposure: Towards out-of-distribution detection in fine-grained environments. In Proceedings of the IEEE\/CVF winter conference on applications of computer vision (WACV) (pp. 5531\u20135540).","DOI":"10.1109\/WACV56688.2023.00549"},{"key":"2117_CR361","unstructured":"Zhang, J., Yang, J., Wang, P., Wang, H., Lin, Y., Zhang, H., Sun, Y., Du, X., Zhou, K., Zhang, W., Li, Y., Liu, Z., Chen, Y., & Li, H. (2023c). Openood v1.5: Enhanced benchmark for out-of-distribution detection. arXiv preprint, arXiv:2306.09301"},{"key":"2117_CR362","unstructured":"Zhang, L., Goldstein, M., & Ranganath, R. (2021). Understanding failures in out-of-distribution detection with deep generative models. In ICML."},{"key":"2117_CR363","unstructured":"Zhao, B., & Han, K. (2021). Novel visual category discovery with dual ranking statistics and mutual knowledge distillation. In NeurIPS."},{"key":"2117_CR364","unstructured":"Zheng, H., Wang, Q., Fang, Z., Xia, X., Liu, F., Liu, T., & Han, B. (2023). Out-of-distribution detection learning with unreliable out-of-distribution sources. In NeurIPS."},{"key":"2117_CR365","doi-asserted-by":"crossref","first-page":"1452","DOI":"10.1109\/TPAMI.2017.2723009","volume":"40","author":"B Zhou","year":"2017","unstructured":"Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., & Torralba, A. (2017). Places: A 10 million image database for scene recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 1452\u20131464.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2117_CR366","doi-asserted-by":"crossref","unstructured":"Zhou, C., Neubig, G., Gu, J., Diab, M., Guzman, P., Zettlemoyer, L., & Ghazvininejad, M. (2020). Detecting hallucinated content in conditional neural sequence generation. In ACL.","DOI":"10.18653\/v1\/2021.findings-acl.120"},{"key":"2117_CR367","doi-asserted-by":"crossref","unstructured":"Zhou, D.-W., Ye, H.-J., & Zhan, D.-C. (2021a). Learning placeholders for open-set recognition. In CVPR.","DOI":"10.1109\/CVPR46437.2021.00438"},{"key":"2117_CR368","doi-asserted-by":"crossref","unstructured":"Zhou, K., Liu, Z., Qiao, Y., Xiang, T., & Loy, C. C. (2021b). Domain generalization: A survey, arXiv preprint arXiv:2103.02503","DOI":"10.1109\/TPAMI.2022.3195549"},{"key":"2117_CR369","doi-asserted-by":"crossref","unstructured":"Zhou, K., Yang, J., Loy, C. C., & Liu, Z. (2022a). Learning to prompt for vision-language models. In International Journal of Computer Vision (IJCV).","DOI":"10.1007\/s11263-022-01653-1"},{"key":"2117_CR370","doi-asserted-by":"crossref","unstructured":"Zhou, K., Yang, J., Loy, C. C., & Liu, Z. (2022b). Conditional prompt learning for vision-language models. In IEEE\/CVF conference on computer vision and pattern recognition (CVPR).","DOI":"10.1109\/CVPR52688.2022.01631"},{"key":"2117_CR371","doi-asserted-by":"crossref","unstructured":"Zhou, Y. (2022). Rethinking reconstruction autoencoder-based out-of-distribution detection. In CVPR.","DOI":"10.1109\/CVPR52688.2022.00723"},{"key":"2117_CR372","doi-asserted-by":"crossref","first-page":"2728","DOI":"10.1109\/TMI.2022.3170077","volume":"41","author":"D Zimmerer","year":"2022","unstructured":"Zimmerer, D., Full, P. M., Isensee, F., J\u00e4ger, P., Adler, T., Petersen, J., K\u00f6hler, G., Ross, T., Reinke, A., Kascenas, A., & Jensen, B. S. (2022). Mood 2020: A public benchmark for out-of-distribution detection and localization on medical images. IEEE Transactions on Medical Imaging, 41, 2728\u20132738.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"2117_CR373","doi-asserted-by":"crossref","unstructured":"Zisselman, E., & Tamar, A. (2020). Deep residual flow for out of distribution detection. In CVPR.","DOI":"10.1109\/CVPR42600.2020.01401"},{"key":"2117_CR374","unstructured":"Zong, B., Song, Q., Min, M. R., Cheng, W., Lumezanu, C., Cho, D., & Chen, H. (2018). Deep autoencoding Gaussian mixture model for unsupervised anomaly detection. In ICLR."}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-024-02117-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11263-024-02117-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-024-02117-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T10:18:14Z","timestamp":1731665894000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11263-024-02117-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,23]]},"references-count":374,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["2117"],"URL":"https:\/\/doi.org\/10.1007\/s11263-024-02117-4","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,23]]},"assertion":[{"value":"27 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 April 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 June 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}