{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T22:34:11Z","timestamp":1783377251635,"version":"3.54.6"},"publisher-location":"Cham","reference-count":48,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031198052","type":"print"},{"value":"9783031198069","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-19806-9_34","type":"book-chapter","created":{"date-parts":[[2022,10,19]],"date-time":"2022-10-19T23:11:54Z","timestamp":1666221114000},"page":"588-604","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["incDFM: Incremental Deep Feature Modeling for\u00a0Continual Novelty Detection"],"prefix":"10.1007","author":[{"given":"Amanda","family":"Rios","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nilesh","family":"Ahuja","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ibrahima","family":"Ndiour","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Utku","family":"Genc","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laurent","family":"Itti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Omesh","family":"Tickoo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,10,20]]},"reference":[{"key":"34_CR1","unstructured":"Ahuja, N.A., Ndiour, I.J., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. In: Bayesian Deep Learning workshop, NeurIPS (2019)"},{"key":"34_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1007\/978-3-030-01219-9_9","volume-title":"Computer Vision \u2013 ECCV 2018","author":"R Aljundi","year":"2018","unstructured":"Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: learning what (not) to forget. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11207, pp. 144\u2013161. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01219-9_9"},{"key":"34_CR3","unstructured":"Aljundi, R., Reino, D.O., Chumerin, N., Turner, R.E.: Continual novelty detection. arXiv preprint arXiv:2106.12964 (2021)"},{"key":"34_CR4","unstructured":"Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. In: Advances in Neural Information Processing Systems, vol. 33, pp. 9912\u20139924 (2020)"},{"key":"34_CR5","unstructured":"Cheung, B., Terekhov, A., Chen, Y., Agrawal, P., Olshausen, B.: Superposition of many models into one. In: Advances in Neural Information Processing Systems 32 (2019)"},{"key":"34_CR6","doi-asserted-by":"crossref","unstructured":"Cohen, G., Afshar, S., Tapson, J., Van Schaik, A.: EMNIST: Extending MNIST to handwritten letters. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 2921\u20132926. IEEE (2017)","DOI":"10.1109\/IJCNN.2017.7966217"},{"key":"34_CR7","unstructured":"De Campos, T.E., Babu, B.R., Varma, M., et al.: Character recognition in natural images. VISAPP (2), 7 (2009)"},{"key":"34_CR8","unstructured":"Dhillon, G.S., Chaudhari, P., Ravichandran, A., Soatto, S.: A baseline for few-shot image classification. arXiv preprint arXiv:1909.02729 (2019)"},{"key":"34_CR9","doi-asserted-by":"crossref","unstructured":"Du, X., Charan, G., Liu, F., Cao, Y.: Single-net continual learning with progressive segmented training. In: 2019 18th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1629\u20131636. IEEE (2019)","DOI":"10.1109\/ICMLA.2019.00267"},{"issue":"1","key":"34_CR10","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","volume":"111","author":"M Everingham","year":"2015","unstructured":"Everingham, M., Eslami, S.A., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes challenge: a retrospective. Int. J. Comput. Vis. 111(1), 98\u2013136 (2015)","journal-title":"Int. J. Comput. Vis."},{"issue":"4","key":"34_CR11","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/S1364-6613(99)01294-2","volume":"3","author":"RM French","year":"1999","unstructured":"French, R.M.: Catastrophic forgetting in connectionist networks. Trends Cognit. Sci. 3(4), 128\u2013135 (1999)","journal-title":"Trends Cognit. Sci."},{"key":"34_CR12","unstructured":"Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: International Conference on Machine Learning, pp. 1050\u20131059 (2016)"},{"key":"34_CR13","unstructured":"Gallardo, J., Hayes, T.L., Kanan, C.: Self-supervised training enhances online continual learning. arXiv preprint arXiv:2103.14010 (2021)"},{"key":"34_CR14","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"34_CR15","unstructured":"Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks (2017)"},{"key":"34_CR16","unstructured":"Hendrycks, D., Mazeika, M., Dietterich, T.: Deep anomaly detection with outlier exposure. In: International Conference on Learning Representations (2019)"},{"key":"34_CR17","doi-asserted-by":"crossref","unstructured":"Hsu, Y.C., Shen, Y., Jin, H., Kira, Z.: Generalized ODIN: detecting out-of-distribution image without learning from out-of-distribution data. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10951\u201310960 (2020)","DOI":"10.1109\/CVPR42600.2020.01096"},{"key":"34_CR18","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"34_CR19","doi-asserted-by":"crossref","unstructured":"Krause, J., Stark, M., Deng, J., Fei-Fei, L.: 3D object representations for fine-grained categorization. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 554\u2013561 (2013)","DOI":"10.1109\/ICCVW.2013.77"},{"key":"34_CR20","unstructured":"Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)"},{"key":"34_CR21","doi-asserted-by":"crossref","unstructured":"Kwon, G., Prabhushankar, M., Temel, D., AlRegib, G.: Novelty detection through model-based characterization of neural networks. In: IEEE International Conference on Image Processing, pp. 3179\u20133183 (2020)","DOI":"10.1109\/ICIP40778.2020.9190706"},{"key":"34_CR22","unstructured":"Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Advances in Neural Information Processing Systems, pp. 6402\u20136413 (2017)"},{"key":"34_CR23","unstructured":"Lee, K., Lee, K., Lee, H., Shin, J.: A simple unified framework for detecting out-of-distribution samples and adversarial attacks. In: Advances in Neural Information Processing Systems, pp. 7167\u20137177 (2018)"},{"key":"34_CR24","unstructured":"Liang, S., Li, Y., Srikant, R.: Enhancing the reliability of out-of-distribution image detection in neural networks (2018)"},{"key":"34_CR25","unstructured":"Lopez-Paz, D., Ranzato, M.: Gradient episodic memory for continual learning. In: Advances in Neural Information Processing Systems 30 (2017)"},{"key":"34_CR26","unstructured":"Maji, S., Rahtu, E., Kannala, J., Blaschko, M., Vedaldi, A.: Fine-grained visual classification of aircraft. arXiv preprint arXiv:1306.5151 (2013)"},{"key":"34_CR27","doi-asserted-by":"crossref","unstructured":"Mallya, A., Lazebnik, S.: PackNet: adding multiple tasks to a single network by iterative pruning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7765\u20137773 (2018)","DOI":"10.1109\/CVPR.2018.00810"},{"key":"34_CR28","unstructured":"Mundt, M., Hong, Y.W., Pliushch, I., Ramesh, V.: A wholistic view of continual learning with deep neural networks: forgotten lessons and the bridge to active and open world learning. arXiv preprint arXiv:2009.01797 (2020)"},{"key":"34_CR29","unstructured":"Ndiour, I., Ahuja, N.A., Tickoo, O.: Out-of-distribution detection with subspace techniques and probabilistic modeling of features. arXiv preprint arXiv:2012.04250 (2020)"},{"key":"34_CR30","unstructured":"Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: Reading digits in natural images with unsupervised feature learning (2011)"},{"key":"34_CR31","doi-asserted-by":"crossref","unstructured":"Nilsback, M.E., Zisserman, A.: Automated flower classification over a large number of classes. In: 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing, pp. 722\u2013729. IEEE (2008)","DOI":"10.1109\/ICVGIP.2008.47"},{"key":"34_CR32","unstructured":"Van den Oord, A., Kalchbrenner, N., Espeholt, L., Vinyals, O., Graves, A., et al.: Conditional image generation with PixelCNN decoders. In: Advances in Neural Information Processing Systems, pp. 4790\u20134798 (2016)"},{"key":"34_CR33","doi-asserted-by":"crossref","unstructured":"Parisi, G., Kemker, R., Part, J., Kanan, C., Wermter, S.: Continual lifelong learning with neural networks: a review. Neural Netw. 113, 54\u201371 (2019)","DOI":"10.1016\/j.neunet.2019.01.012"},{"issue":"4","key":"34_CR34","doi-asserted-by":"publisher","first-page":"715","DOI":"10.1037\/0033-295X.112.4.715","volume":"112","author":"AA Petrov","year":"2005","unstructured":"Petrov, A.A., Dosher, B.A., Lu, Z.L.: The dynamics of perceptual learning: an incremental reweighting model. Psychol. Rev. 112(4), 715 (2005)","journal-title":"Psychol. Rev."},{"key":"34_CR35","doi-asserted-by":"crossref","unstructured":"Quattoni, A., Torralba, A.: Recognizing indoor scenes. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 413\u2013420. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206537"},{"key":"34_CR36","doi-asserted-by":"crossref","unstructured":"Rebuffi, S.A., Kolesnikov, A., Sperl, G., Lampert, C.H.: iCaRL: incremental classifier and representation learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2001\u20132010 (2017)","DOI":"10.1109\/CVPR.2017.587"},{"key":"34_CR37","unstructured":"Ren, J., et al.: Likelihood ratios for out-of-distribution detection. In: Advances in Neural Information Processing Systems, pp. 14707\u201314718 (2019)"},{"key":"34_CR38","doi-asserted-by":"crossref","unstructured":"Rios, A., Itti, L.: Closed-loop memory GAN for continual learning. arXiv preprint arXiv:1811.01146 (2018)","DOI":"10.24963\/ijcai.2019\/462"},{"key":"34_CR39","doi-asserted-by":"crossref","unstructured":"Rios, A., Itti, L.: Lifelong learning without a task oracle. In: 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI), pp. 255\u2013263. IEEE (2020)","DOI":"10.1109\/ICTAI50040.2020.00049"},{"key":"34_CR40","unstructured":"Rolnick, D., Ahuja, A., Schwarz, J., Lillicrap, T., Wayne, G.: Experience replay for continual learning. In: Advances in Neural Information Processing Systems 32 (2019)"},{"key":"34_CR41","unstructured":"Shin, H., Lee, J.K., Kim, J., Kim, J.: Continual learning with deep generative replay. In: Advances in Neural Information Processing Systems 30 (2017)"},{"key":"34_CR42","unstructured":"Sun, J., et al.: Gradient-based novelty detection boosted by self-supervised binary classification. arXiv preprint arXiv:2112.09815 (2021)"},{"key":"34_CR43","doi-asserted-by":"crossref","unstructured":"Van Horn, G., Cole, E., Beery, S., Wilber, K., Belongie, S., Mac Aodha, O.: Benchmarking representation learning for natural world image collections. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12884\u201312893 (2021)","DOI":"10.1109\/CVPR46437.2021.01269"},{"key":"34_CR44","unstructured":"Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: The caltech-ucsd birds-200-2011 dataset (2011)"},{"key":"34_CR45","doi-asserted-by":"crossref","unstructured":"Wen, S., Rios, A., Ge, Y., Itti, L.: Beneficial perturbation network for designing general adaptive artificial intelligence systems. IEEE Trans. Neural Netw. Learn. Syst. (2021)","DOI":"10.1109\/TNNLS.2021.3054423"},{"key":"34_CR46","unstructured":"Wu, C., Herranz, L., Liu, X., van de Weijer, J., Raducanu, B., et al.: Memory replay GANs: learning to generate new categories without forgetting. In: Advances in Neural Information Processing Systems 31 (2018)"},{"key":"34_CR47","unstructured":"Yoon, J., Yang, E., Lee, J., Hwang, S.J.: Lifelong learning with dynamically expandable networks (2018)"},{"issue":"8","key":"34_CR48","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1038\/s42256-019-0080-x","volume":"1","author":"G Zeng","year":"2019","unstructured":"Zeng, G., Chen, Y., Cui, B., Yu, S.: Continual learning of context-dependent processing in neural networks. Nat. Mach. Intell. 1(8), 364\u2013372 (2019)","journal-title":"Nat. Mach. Intell."}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-19806-9_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T23:15:32Z","timestamp":1666394132000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19806-9_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031198052","9783031198069"],"references-count":48,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19806-9_34","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"20 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5804","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1645","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"28% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.21","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.91","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}