{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T12:01:16Z","timestamp":1767873676069,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":51,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819557011","type":"print"},{"value":"9789819557028","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-5702-8_6","type":"book-chapter","created":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T08:29:44Z","timestamp":1767860984000},"page":"78-92","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Frustratingly Easy Feature Reconstruction for\u00a0Out-of-Distribution Detection"],"prefix":"10.1007","author":[{"given":"Yingsheng","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuo","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aihua","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ran","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,9]]},"reference":[{"key":"6_CR1","unstructured":"Ammar, M.B., Belkhir, N., Popescu, S., Manzanera, A., Franchi, G.: NECO: Neural collapse based out-of-distribution detection. In: Proceedings of the ICLR (2024)"},{"key":"6_CR2","unstructured":"Basart, S., Mantas, M., Mohammadreza, M., Jacob, S., Dawn, S.: Scaling out-of-distribution detection for real-world settings. In: Proceedings of the ICML (2022)"},{"key":"6_CR3","doi-asserted-by":"crossref","unstructured":"Chan, R., Rottmann, M., Gottschalk, H.: Entropy maximization and meta classification for out-of-distribution detection in semantic segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5128\u20135137 (2021)","DOI":"10.1109\/ICCV48922.2021.00508"},{"key":"6_CR4","doi-asserted-by":"crossref","unstructured":"Chen, Y.H., Chen, W.Y., Chen, Y.T., Tsai, B.C., Frank\u00a0Wang, Y.C., Sun, M.: No more discrimination: cross city adaptation of road scene segmenters. In: Proceedings of the ICCV, pp. 1992\u20132001 (2017)","DOI":"10.1109\/ICCV.2017.220"},{"key":"6_CR5","doi-asserted-by":"crossref","unstructured":"Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., Vedaldi, A.: Describing textures in the wild. In: Proceedings of the CVPR, pp. 3606\u20133613 (2014)","DOI":"10.1109\/CVPR.2014.461"},{"key":"6_CR6","unstructured":"Cook, M., Zare, A., Gader, P.: Outlier detection through null space analysis of neural networks. arXiv preprint arXiv:2007.01263 (2020)"},{"key":"6_CR7","unstructured":"Daniel, C.d.C., Ian, W., Ben, G.: Causality matters in medical imaging. Nat. Commun. (2020)"},{"key":"6_CR8","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: Proceedings of the CVPR, pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"6_CR9","unstructured":"Djurisic, A., Bozanic, N., Ashok, A., Liu, R.: Extremely simple activation shaping for out-of-distribution detection. In: Proceedings of the ICLR (2023)"},{"key":"6_CR10","unstructured":"Dosovitskiy, A., et\u00a0al.: An image is worth 16x16 words: transformers for image recognition at scale. In: Proceedings of the ICLR (2020)"},{"key":"6_CR11","doi-asserted-by":"crossref","unstructured":"Fang, K., Tao, Q., Lv, K., He, M., Huang, X., Yang, J.: Kernel PCA for out-of-distribution detection. In: Proceedings of the NeruIPS (2024)","DOI":"10.52202\/079017-4268"},{"key":"6_CR12","doi-asserted-by":"crossref","unstructured":"Guan, X., Liu, Z., Zheng, W.S., Zhou, Y., Wang, R.: Revisit PCA-based technique for out-of-distribution detection. In: Proceedings of the ICCV, pp. 19431\u201319439 (2023)","DOI":"10.1109\/ICCV51070.2023.01780"},{"key":"6_CR13","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the CVPR, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"6_CR14","doi-asserted-by":"crossref","unstructured":"He, R., Hu, B., Zheng, W.S., Guo, Y.: Two-stage sparse representation for robust recognition on large-scale database. In: Proceedings of the AAAI, vol.\u00a024, pp. 475\u2013480 (2010)","DOI":"10.1609\/aaai.v24i1.7654"},{"issue":"12","key":"6_CR15","doi-asserted-by":"publisher","first-page":"5543","DOI":"10.1109\/TIP.2015.2466106","volume":"24","author":"R He","year":"2015","unstructured":"He, R., Zhang, M., Wang, L., Ji, Y., Yin, Q.: Cross-modal subspace learning via pairwise constraints. IEEE Trans. Image Process. 24(12), 5543\u20135556 (2015)","journal-title":"IEEE Trans. Image Process."},{"key":"6_CR16","doi-asserted-by":"crossref","unstructured":"Hein, M., Andriushchenko, M., Bitterwolf, J.: Why RELU networks yield high-confidence predictions far away from the training data and how to mitigate the problem. In: Proceedings of the CVPR, pp. 41\u201350 (2019)","DOI":"10.1109\/CVPR.2019.00013"},{"key":"6_CR17","unstructured":"Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: Proceedings of the ICLR (2017)"},{"key":"6_CR18","unstructured":"Huang, R., Geng, A., Li, Y.: On the importance of gradients for detecting distributional shifts in the wild. In: Proceedings of the NeurIPS, vol.\u00a034, pp. 677\u2013689 (2021)"},{"key":"6_CR19","unstructured":"Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. In: Handbook of Systemic Autoimmune Diseases, vol. 1, no. 4 (2009)"},{"key":"6_CR20","unstructured":"Lee, K., Lee, K., Lee, H., Shin, J.: A simple unified framework for detecting out-of-distribution samples and adversarial attacks. In: Proceedings of the NeurIPS, vol.\u00a031 (2018)"},{"key":"6_CR21","unstructured":"Liang, J., Sheng, L., Wang, Z., He, R., Tan, T.: Realistic unsupervised clip fine-tuning with universal entropy optimization. In: Proceedings of the ICML (2024)"},{"key":"6_CR22","unstructured":"Liang, S., Li, Y., Srikant, R.: Enhancing the reliability of out-of-distribution image detection in neural networks. In: Proceedings of the ICLR (2018)"},{"key":"6_CR23","unstructured":"Liu, W., Wang, X., Owens, J., Li, Y.: Energy-based out-of-distribution detection. In: Proceedings of the NeurIPS, vol. 33, pp. 21464\u201321475 (2020)"},{"issue":"7","key":"6_CR24","doi-asserted-by":"publisher","first-page":"1897","DOI":"10.1109\/TMI.2022.3150435","volume":"41","author":"X Liu","year":"2022","unstructured":"Liu, X., Yuan, Y.: A source-free domain adaptive polyp detection framework with style diversification flow. IEEE Trans. Med. Imaging 41(7), 1897\u20131908 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"6_CR25","unstructured":"Lu, H., Gong, D., Wang, S., Xue, J., Yao, L., Moore, K.: Learning with mixture of prototypes for out-of-distribution detection. In: Proceedings of the ICLR (2024)"},{"key":"6_CR26","doi-asserted-by":"publisher","DOI":"10.1145\/3760390","author":"S Lu","year":"2025","unstructured":"Lu, S., Wang, Y., Sheng, L., He, L., Zheng, A., Liang, J.: Out-of-distribution detection: a task-oriented survey of recent advances. ACM Comput. Surv. (2025). https:\/\/doi.org\/10.1145\/3760390","journal-title":"ACM Comput. Surv."},{"key":"6_CR27","unstructured":"Ming, Y., Sun, Y., Dia, O., Li, Y.: How to exploit hyperspherical embeddings for out-of-distribution detection? In: Proceedings of the ICLR (2023)"},{"key":"6_CR28","doi-asserted-by":"crossref","unstructured":"Morteza, P., Li, Y.: Provable guarantees for understanding out-of-distribution detection. In: Proceedings of the AAAI, vol.\u00a036, pp. 7831\u20137840 (2022)","DOI":"10.1609\/aaai.v36i7.20752"},{"key":"6_CR29","unstructured":"Mueller, M., Hein, M.: Mahalanobis++: improving OOD detection via feature normalization. In: Proceedings of the ICML (2025)"},{"key":"6_CR30","unstructured":"Netzer, Y., et\u00a0al.: Reading digits in natural images with unsupervised feature learning. In: Proceedings of the NeurIPS, vol.\u00a02011, p..\u00a07 (2011)"},{"key":"6_CR31","doi-asserted-by":"crossref","unstructured":"Park, J., Jung, Y.G., Teoh, A.B.J.: Nearest neighbor guidance for out-of-distribution detection. In: Proceedings of the ICCV, pp. 1686\u20131695 (2023)","DOI":"10.1109\/ICCV51070.2023.00162"},{"key":"6_CR32","unstructured":"Peng, B., Luo, Y., Zhang, Y., Li, Y., Fang, Z.: ConjNorm: tractable density estimation for out-of-distribution detection. In: Proceedings of the ICLR (2024)"},{"key":"6_CR33","doi-asserted-by":"crossref","unstructured":"Pu, Y., Feng, Z., Wang, Z., Yang, Z., Li, J.: Anomaly detection for in situ marine plankton images. In: Proceedings of the ICCV, pp. 3661\u20133671 (2021)","DOI":"10.1109\/ICCVW54120.2021.00409"},{"key":"6_CR34","doi-asserted-by":"crossref","unstructured":"Qu, S., et al.: LEAD: learning decomposition for source-free universal domain adaptation. In: Proceedings of the CVPR, pp. 23334\u201323343 (2024)","DOI":"10.1109\/CVPR52733.2024.02202"},{"key":"6_CR35","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetV2: inverted residuals and linear bottlenecks. In: Proceedings of the CVPR, pp. 4510\u20134520 (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"6_CR36","unstructured":"Sehwag, V., Chiang, M., Mittal, P.: SSD: a unified framework for self-supervised outlier detection. In: Proceedings of the ICLR (2021)"},{"key":"6_CR37","first-page":"2853","volume":"29","author":"L Shan","year":"2021","unstructured":"Shan, L., Yichao, T., Ying, T., Hansong, S.: Visual driving assistance system based on few-shot learning. Multimedia Syst. 29, 2853\u20132863 (2021)","journal-title":"Multimedia Syst."},{"key":"6_CR38","unstructured":"Sun, Y., Guo, C., Li, Y.: ReAct: out-of-distribution detection with rectified activations. In: Proceedings of the NeurIPS (2021)"},{"key":"6_CR39","doi-asserted-by":"publisher","unstructured":"Sun, Y., Li, Y.: Dice: Leveraging sparsification for out-of-distribution detection. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision, ECCV 2022. LNCS, vol. 13684, pp. 691\u2013708. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-20053-3_40","DOI":"10.1007\/978-3-031-20053-3_40"},{"key":"6_CR40","unstructured":"Sun, Y., Ming, Y., Zhu, X., Li, Y.: Out-of-distribution detection with deep nearest neighbors. In: Proceedings of the ICML, pp. 20827\u201320840. PMLR (2022)"},{"key":"6_CR41","unstructured":"Tanwisuth, K., Fan, X., Zheng, H., Zhang, S., Zhang, H., Chen, B., Zhou, M.: A prototype-oriented framework for unsupervised domain adaptation. In: Proceedings of the NeurIPS, vol. 34, pp. 17194\u201317208 (2021)"},{"key":"6_CR42","doi-asserted-by":"crossref","unstructured":"Van\u00a0Horn, G., et al.: The iNaturalist species classification and detection dataset. In: Proceedings of the CVPR, pp. 8769\u20138778 (2018)","DOI":"10.1109\/CVPR.2018.00914"},{"key":"6_CR43","doi-asserted-by":"crossref","unstructured":"Wang, H., Li, Z., Feng, L., Zhang, W.: ViM: out-of-distribution with virtual-logit matching. In: Proceedings of the CVPR, pp. 4921\u20134930 (2022)","DOI":"10.1109\/CVPR52688.2022.00487"},{"key":"6_CR44","doi-asserted-by":"crossref","unstructured":"Xiao, J., Hays, J., Ehinger, K.A., Oliva, A., Torralba, A.: Sun database: large-scale scene recognition from abbey to zoo. In: Proceedings of the CVPR, pp. 3485\u20133492. IEEE (2010)","DOI":"10.1109\/CVPR.2010.5539970"},{"key":"6_CR45","unstructured":"Yang, J., et\u00a0al.: OpenOOD: benchmarking generalized out-of-distribution detection. In: Proceedings of the NeurIPS, vol.\u00a035, pp. 32598\u201332611 (2022)"},{"key":"6_CR46","doi-asserted-by":"publisher","first-page":"5635","DOI":"10.1007\/s11263-024-02117-4","volume":"132","author":"J Yang","year":"2024","unstructured":"Yang, J., Zhou, K., Li, Y., Liu, Z.: Generalized out-of-distribution detection: a survey. IJCV 132, 5635\u20135662 (2024). https:\/\/doi.org\/10.1007\/s11263-024-02117-4","journal-title":"IJCV"},{"key":"6_CR47","unstructured":"Yang, P., Liang, J., Cao, J., He, R.: AUTO: adaptive outlier optimization for online test-time OOD detection. arXiv preprint arXiv:2303.12267 (2023)"},{"key":"6_CR48","unstructured":"Zhang, J., et\u00a0al.: OpenOOD v1.5: enhanced benchmark for out-of-distribution detection. arXiv preprint arXiv:2306.09301 (2023)"},{"key":"6_CR49","unstructured":"Zhao, Q., Xu, M., Gupta, K., Asthana, A., Zheng, L., Gould, S.: Towards optimal feature-shaping methods for out-of-distribution detection. In: Proceedings of the ICLR (2024)"},{"issue":"6","key":"6_CR50","doi-asserted-by":"publisher","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.: Places: a 10 million image database for scene recognition. IEEE TPAMI 40(6), 1452\u20131464 (2017)","journal-title":"IEEE TPAMI"},{"key":"6_CR51","unstructured":"Zhu, Y., et\u00a0al.: Boosting out-of-distribution detection with typical features. In: NeurIPS, vol.\u00a035, pp. 20758\u201320769 (2022)"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-5702-8_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T08:30:01Z","timestamp":1767861001000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-5702-8_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819557011","9789819557028"],"references-count":51,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-5702-8_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"9 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shanghai","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2025.prcv.cn\/index.asp","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}