{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T19:03:50Z","timestamp":1757703830031,"version":"3.40.3"},"publisher-location":"Cham","reference-count":45,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031198298"},{"type":"electronic","value":"9783031198304"}],"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\/978-3-031-19830-4_16","type":"book-chapter","created":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T16:21:10Z","timestamp":1666369270000},"page":"269-285","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Personalized Education: Blind Knowledge Distillation"],"prefix":"10.1007","author":[{"given":"Xiang","family":"Deng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongfei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,10,22]]},"reference":[{"key":"16_CR1","unstructured":"Aguilar, G., Ling, Y., Zhang, Y., Yao, B., Fan, X., Guo, E.: Knowledge distillation from internal representations. arXiv preprint arXiv:1910.03723 (2019)"},{"key":"16_CR2","doi-asserted-by":"crossref","unstructured":"Ahn, S., Hu, S.X., Damianou, A., Lawrence, N.D., Dai, Z.: Variational information distillation for knowledge transfer. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9163\u20139171 (2019)","DOI":"10.1109\/CVPR.2019.00938"},{"key":"16_CR3","doi-asserted-by":"crossref","unstructured":"Chen, L., Wang, D., Gan, Z., Liu, J., Henao, R., Carin, L.: Wasserstein contrastive representation distillation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16296\u201316305 (2021)","DOI":"10.1109\/CVPR46437.2021.01603"},{"key":"16_CR4","doi-asserted-by":"crossref","unstructured":"Chen, P., Liu, S., Zhao, H., Jia, J.: Distilling knowledge via knowledge review. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5008\u20135017 (2021)","DOI":"10.1109\/CVPR46437.2021.00497"},{"key":"16_CR5","doi-asserted-by":"crossref","unstructured":"Cho, J.H., Hariharan, B.: On the efficacy of knowledge distillation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4794\u20134802 (2019)","DOI":"10.1109\/ICCV.2019.00489"},{"key":"16_CR6","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: CVPR09 (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"16_CR7","unstructured":"Deng, X., Zhang, Z.: Comprehensive knowledge distillation with causal intervention. Adv. Neural Inf. Process. Syst. 34, 22158\u201322170 (2021)"},{"key":"16_CR8","doi-asserted-by":"crossref","unstructured":"Deng, X., Zhang, Z.: Graph-free knowledge distillation for graph neural networks. In: Proceedings of the 30th International Joint Conference on Artificial Intelligence (2021)","DOI":"10.24963\/ijcai.2021\/320"},{"key":"16_CR9","doi-asserted-by":"crossref","unstructured":"Deng, X., Zhang, Z.: Learning with retrospection. In: Proceedings of the AAAI Conference on Artificial Intelligence (2021)","DOI":"10.1609\/aaai.v35i8.16885"},{"key":"16_CR10","unstructured":"Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y.: Deep Learning, vol. 1. MIT Press, Cambridge (2016)"},{"key":"16_CR11","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672\u20132680 (2014)"},{"key":"16_CR12","doi-asserted-by":"publisher","first-page":"871","DOI":"10.1109\/TIP.2019.2936742","volume":"29","author":"X Han","year":"2019","unstructured":"Han, X., Song, X., Yao, Y., Xu, X.S., Nie, L.: Neural compatibility modeling with probabilistic knowledge distillation. IEEE Trans. Image Process. 29, 871\u2013882 (2019)","journal-title":"IEEE Trans. Image Process."},{"key":"16_CR13","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":"16_CR14","doi-asserted-by":"crossref","unstructured":"Heo, B., Kim, J., Yun, S., Park, H., Kwak, N., Choi, J.Y.: A comprehensive overhaul of feature distillation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1921\u20131930 (2019)","DOI":"10.1109\/ICCV.2019.00201"},{"key":"16_CR15","doi-asserted-by":"crossref","unstructured":"Heo, B., Lee, M., Yun, S., Choi, J.Y.: Knowledge transfer via distillation of activation boundaries formed by hidden neurons. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 3779\u20133787 (2019)","DOI":"10.1609\/aaai.v33i01.33013779"},{"key":"16_CR16","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)"},{"key":"16_CR17","unstructured":"Huang, Z., Wang, N.: Like what you like: Knowledge distill via neuron selectivity transfer. arXiv preprint arXiv:1707.01219 (2017)"},{"key":"16_CR18","doi-asserted-by":"crossref","unstructured":"Huang, Z., et al.: Revisiting knowledge distillation: an inheritance and exploration framework. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3579\u20133588 (2021)","DOI":"10.1109\/CVPR46437.2021.00358"},{"key":"16_CR19","unstructured":"Yang, J., Martinez, B., Bulat, A., Tzimiropoulos, G.: Knowledge distillation vis softmax regression representation learning. In: International Conference on Learning Representations (2021)"},{"key":"16_CR20","unstructured":"Kim, J., Park, S., Kwak, N.: Paraphrasing complex network: Network compression via factor transfer. In: Advances in Neural Information Processing Systems, pp. 2760\u20132769 (2018)"},{"key":"16_CR21","unstructured":"Koratana, A., Kang, D., Bailis, P., Zaharia, M.: Lit: Learned intermediate representation training for model compression. In: International Conference on Machine Learning, pp. 3509\u20133518 (2019)"},{"key":"16_CR22","unstructured":"Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Technical Report, Citeseer (2009)"},{"key":"16_CR23","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097\u20131105 (2012)"},{"key":"16_CR24","doi-asserted-by":"publisher","first-page":"4735","DOI":"10.1109\/TIP.2021.3066051","volume":"30","author":"X Li","year":"2021","unstructured":"Li, X., Li, S., Omar, B., Wu, F., Li, X.: Reskd: residual-guided knowledge distillation. IEEE Trans. Image Process. 30, 4735\u20134746 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"16_CR25","unstructured":"Liu, R., Fusi, N., Mackey, L.: Teacher-student compression with generative adversarial networks. arXiv preprint arXiv:1812.02271 (2018)"},{"key":"16_CR26","doi-asserted-by":"crossref","unstructured":"Ma, N., Zhang, X., Zheng, H.T., Sun, J.: Shufflenet v2: practical guidelines for efficient CNN architecture design. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 116\u2013131 (2018)","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"16_CR27","doi-asserted-by":"crossref","unstructured":"Mirzadeh, S.I., Farajtabar, M., Li, A., Levine, N., Matsukawa, A., Ghasemzadeh, H.: Improved knowledge distillation via teacher assistant. In: AAAI Conference on Artificial Intelligence (2020)","DOI":"10.1609\/aaai.v34i04.5963"},{"key":"16_CR28","doi-asserted-by":"crossref","unstructured":"Park, W., Kim, D., Lu, Y., Cho, M.: Relational knowledge distillation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3967\u20133976 (2019)","DOI":"10.1109\/CVPR.2019.00409"},{"key":"16_CR29","doi-asserted-by":"crossref","unstructured":"Passalis, N., Tefas, A.: Learning deep representations with probabilistic knowledge transfer. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 268\u2013284 (2018)","DOI":"10.1007\/978-3-030-01252-6_17"},{"key":"16_CR30","doi-asserted-by":"crossref","unstructured":"Peng, B., et al.: Correlation congruence for knowledge distillation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5007\u20135016 (2019)","DOI":"10.1109\/ICCV.2019.00511"},{"key":"16_CR31","unstructured":"Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., Bengio, Y.: Fitnets: hints for thin deep nets. In: International Conference on Learning Representations (2015)"},{"key":"16_CR32","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (2015)"},{"key":"16_CR33","unstructured":"Srinivas, S., Fleuret, F.: Knowledge transfer with Jacobian matching. In: Dy, J., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 80, pp. 4723\u20134731. PMLR, Stockholmsm\u00e4ssan, Stockholm Sweden, 10\u201315 July 2018"},{"key":"16_CR34","unstructured":"Tian, Y., Krishnan, D., Isola, P.: Contrastive representation distillation. In: International Conference on Learning Representations (2020)"},{"key":"16_CR35","doi-asserted-by":"crossref","unstructured":"Tung, F., Mori, G.: Similarity-preserving knowledge distillation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1365\u20131374 (2019)","DOI":"10.1109\/ICCV.2019.00145"},{"key":"16_CR36","doi-asserted-by":"crossref","unstructured":"Wang, D., Li, Y., Wang, L., Gong, B.: Neural networks are more productive teachers than human raters: active mixup for data-efficient knowledge distillation from a blackbox model. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1498\u20131507 (2020)","DOI":"10.1109\/CVPR42600.2020.00157"},{"key":"16_CR37","unstructured":"Wang, X., Zhang, R., Sun, Y., Qi, J.: Kdgan: Knowledge distillation with generative adversarial networks. In: Advances in Neural Information Processing Systems, pp. 775\u2013786 (2018)"},{"key":"16_CR38","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"588","DOI":"10.1007\/978-3-030-58545-7_34","volume-title":"Computer Vision","author":"G Xu","year":"2020","unstructured":"Xu, G., Liu, Z., Li, X., Loy, C.C.: Knowledge distillation meets self-supervision. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12354, pp. 588\u2013604. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58545-7_34"},{"key":"16_CR39","doi-asserted-by":"crossref","unstructured":"Yim, J., Joo, D., Bae, J., Kim, J.: A gift from knowledge distillation: fast optimization, network minimization and transfer learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4133\u20134141 (2017)","DOI":"10.1109\/CVPR.2017.754"},{"key":"16_CR40","doi-asserted-by":"crossref","unstructured":"Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., Yoo, Y.: Cutmix: regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp. 6023\u20136032 (2019)","DOI":"10.1109\/ICCV.2019.00612"},{"key":"16_CR41","doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., Komodakis, N.: Wide residual networks. In: BMVC (2016)","DOI":"10.5244\/C.30.87"},{"key":"16_CR42","unstructured":"Zagoruyko, S., Komodakis, N.: Paying more attention to attention: improving the performance of convolutional neural networks via attention transfer. In: International Conference on Learning Representations (2017)"},{"key":"16_CR43","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: beyond empirical risk minimization. International Conference on Learning Representations (2018). https:\/\/openreview.net\/forum?id=r1Ddp1-Rb"},{"key":"16_CR44","unstructured":"Zhou, H., et al.: Rethinking soft labels for knowledge distillation: a bias-variance tradeoff perspective. In: International Conference on Learning Representations (2021)"},{"key":"16_CR45","doi-asserted-by":"crossref","unstructured":"Zhu, J., et al.: Complementary relation contrastive distillation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9260\u20139269 (2021)","DOI":"10.1109\/CVPR46437.2021.00914"}],"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\/978-3-031-19830-4_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T23:53:43Z","timestamp":1666396423000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19830-4_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031198298","9783031198304"],"references-count":45,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19830-4_16","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":"22 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)"}}]}}