{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T19:51:22Z","timestamp":1743018682510,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031197772"},{"type":"electronic","value":"9783031197789"}],"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\/978-3-031-19778-9_25","type":"book-chapter","created":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T20:28:41Z","timestamp":1667420921000},"page":"433-448","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Attaining Class-Level Forgetting in\u00a0Pretrained Model Using Few Samples"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1001-2219","authenticated-orcid":false,"given":"Pravendra","family":"Singh","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1103-1884","authenticated-orcid":false,"given":"Pratik","family":"Mazumder","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9664-8706","authenticated-orcid":false,"given":"Mohammed Asad","family":"Karim","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,3]]},"reference":[{"key":"25_CR1","doi-asserted-by":"crossref","unstructured":"Carreira-Perpin\u00e1n, M.A., Idelbayev, Y.: \u201clearning-compression\u201d algorithms for neural net pruning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8532\u20138541 (2018)","DOI":"10.1109\/CVPR.2018.00890"},{"key":"25_CR2","unstructured":"Dong, X., Chen, S., Pan, S.J.: Learning to prune deep neural networks via layer-wise optimal brain surgeon. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS 2017, pp. 4860\u20134874. Curran Associates Inc., Red Hook (2017)"},{"key":"25_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1007\/978-3-030-58565-5_6","volume-title":"Computer Vision \u2013 ECCV 2020","author":"A Douillard","year":"2020","unstructured":"Douillard, A., Cord, M., Ollion, C., Robert, T., Valle, E.: PODNet: pooled outputs distillation for small-tasks incremental learning. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12365, pp. 86\u2013102. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58565-5_6"},{"key":"25_CR4","unstructured":"Edwards, H., Storkey, A.: Censoring representations with an adversary. arXiv preprint arXiv:1511.05897 (2015)"},{"key":"25_CR5","unstructured":"Ginart, A., Guan, M.Y., Valiant, G., Zou, J.: Making AI forget you: data deletion in machine learning. arXiv preprint arXiv:1907.05012 (2019)"},{"key":"25_CR6","doi-asserted-by":"crossref","unstructured":"Golatkar, A., Achille, A., Ravichandran, A., Polito, M., Soatto, S.: Mixed-privacy forgetting in deep networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 792\u2013801 (2021)","DOI":"10.1109\/CVPR46437.2021.00085"},{"key":"25_CR7","first-page":"1379","volume":"29","author":"Y Guo","year":"2016","unstructured":"Guo, Y., Yao, A., Chen, Y.: Dynamic network surgery for efficient DNNs. Adv. Neural. Inf. Process. Syst. 29, 1379\u20131387 (2016)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"1","key":"25_CR8","first-page":"4704","volume":"18","author":"J Hamm","year":"2017","unstructured":"Hamm, J.: Minimax filter: learning to preserve privacy from inference attacks. J. Mach. Learn. Res. 18(1), 4704\u20134734 (2017)","journal-title":"J. Mach. Learn. Res."},{"key":"25_CR9","unstructured":"Han, S., Mao, H., Dally, W.J.: Deep compression: compressing deep neural networks with pruning, trained quantization and Huffman coding. arXiv preprint arXiv:1510.00149 (2015)"},{"key":"25_CR10","doi-asserted-by":"crossref","unstructured":"He, Y., Kang, G., Dong, X., Fu, Y., Yang, Y.: Soft filter pruning for accelerating deep convolutional neural networks. In: IJCAI International Joint Conference on Artificial Intelligence (2018)","DOI":"10.24963\/ijcai.2018\/309"},{"issue":"8","key":"25_CR11","doi-asserted-by":"publisher","first-page":"3594","DOI":"10.1109\/TCYB.2019.2933477","volume":"50","author":"Y He","year":"2019","unstructured":"He, Y., Dong, X., Kang, G., Fu, Y., Yan, C., Yang, Y.: Asymptotic soft filter pruning for deep convolutional neural networks. IEEE Trans. Cybern. 50(8), 3594\u20133604 (2019)","journal-title":"IEEE Trans. Cybern."},{"key":"25_CR12","doi-asserted-by":"crossref","unstructured":"He, Y., Liu, P., Wang, Z., Hu, Z., Yang, Y.: Filter pruning via geometric median for deep convolutional neural networks acceleration. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4340\u20134349 (2019)","DOI":"10.1109\/CVPR.2019.00447"},{"key":"25_CR13","doi-asserted-by":"crossref","unstructured":"He, Y., Liu, P., Zhu, L., Yang, Y.: Meta filter pruning to accelerate deep convolutional neural networks. arXiv preprint arXiv:1904.03961 (2019)","DOI":"10.1109\/CVPR42600.2020.00208"},{"key":"25_CR14","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. In: NIPS Deep Learning and Representation Learning Workshop (2014). https:\/\/fb56552f-a-62cb3a1a-s-sites.googlegroups.com\/site\/deeplearningworkshopnips2014\/65.pdf"},{"key":"25_CR15","doi-asserted-by":"crossref","unstructured":"Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831\u2013839 (2019)","DOI":"10.1109\/CVPR.2019.00092"},{"key":"25_CR16","unstructured":"Kemker, R., Kanan, C.: FearNet: brain-inspired model for incremental learning. In: International Conference on Learning Representations (2018). https:\/\/openreview.net\/forum?id=SJ1Xmf-Rb"},{"key":"25_CR17","unstructured":"Li, H., Kadav, A., Durdanovic, I., Samet, H., Graf, H.P.: Pruning filters for efficient convnets. arXiv preprint arXiv:1608.08710 (2016)"},{"key":"25_CR18","doi-asserted-by":"crossref","unstructured":"Liu, Y., Schiele, B., Sun, Q.: Adaptive aggregation networks for class-incremental learning. In: The IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)","DOI":"10.1109\/CVPR46437.2021.00257"},{"key":"25_CR19","unstructured":"Louizos, C., Swersky, K., Li, Y., Welling, M., Zemel, R.: The variational fair autoencoder. arXiv preprint arXiv:1511.00830 (2015)"},{"key":"25_CR20","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.jpdc.2019.09.010","volume":"137","author":"L Nan","year":"2020","unstructured":"Nan, L., Tao, D.: Variational approach for privacy funnel optimization on continuous data. J. Parallel Distrib. Comput. 137, 17\u201325 (2020)","journal-title":"J. Parallel Distrib. Comput."},{"key":"25_CR21","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":"25_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1007\/978-3-030-58529-7_16","volume-title":"Computer Vision \u2013 ECCV 2020","author":"X Tao","year":"2020","unstructured":"Tao, X., Chang, X., Hong, X., Wei, X., Gong, Y.: Topology-preserving class-incremental learning. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12364, pp. 254\u2013270. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58529-7_16"},{"key":"25_CR23","doi-asserted-by":"crossref","unstructured":"Wu, Y., et al.: Large scale incremental learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 374\u2013382 (2019)","DOI":"10.1109\/CVPR.2019.00046"},{"key":"25_CR24","doi-asserted-by":"crossref","unstructured":"Yu, L., et al.: Semantic drift compensation for class-incremental learning. In: CVPR, pp. 6982\u20136991 (2020)","DOI":"10.1109\/CVPR42600.2020.00701"},{"key":"25_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1007\/978-3-030-01237-3_12","volume-title":"Computer Vision \u2013 ECCV 2018","author":"T Zhang","year":"2018","unstructured":"Zhang, T., et al.: A systematic DNN weight pruning framework using alternating direction method of multipliers. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11212, pp. 191\u2013207. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01237-3_12"}],"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\/978-3-031-19778-9_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T20:58:11Z","timestamp":1667422691000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19778-9_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031197772","9783031197789"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19778-9_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"3 November 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)"}}]}}