{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T17:43:05Z","timestamp":1772300585286,"version":"3.50.1"},"publisher-location":"Cham","reference-count":42,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031262838","type":"print"},{"value":"9783031262845","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-26284-5_31","type":"book-chapter","created":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T08:02:59Z","timestamp":1677052979000},"page":"507-525","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["What Role Does Data Augmentation Play in\u00a0Knowledge Distillation?"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9235-9429","authenticated-orcid":false,"given":"Wei","family":"Li","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4689-6140","authenticated-orcid":false,"given":"Shitong","family":"Shao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4706-6846","authenticated-orcid":false,"given":"Weiyan","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7287-5936","authenticated-orcid":false,"given":"Ziming","family":"Qiu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4063-6009","authenticated-orcid":false,"given":"Zhihao","family":"Zhu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8708-5814","authenticated-orcid":false,"given":"Wei","family":"Huan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,23]]},"reference":[{"key":"31_CR1","doi-asserted-by":"crossref","unstructured":"Beyer, L., Zhai, X., Royer, A., Markeeva, L., Anil, R., Kolesnikov, A.: Knowledge distillation: a good teacher is patient and consistent. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10925\u201310934 (2022)","DOI":"10.1109\/CVPR52688.2022.01065"},{"key":"31_CR2","doi-asserted-by":"crossref","unstructured":"Chen, D., Mei, J.P., Wang, C., Feng, Y., Chen, C.: Online knowledge distillation with diverse peers. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 3430\u20133437 (2020)","DOI":"10.1609\/aaai.v34i04.5746"},{"key":"31_CR3","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597\u20131607. PMLR (2020)"},{"key":"31_CR4","doi-asserted-by":"crossref","unstructured":"Yang, C., An, Z., Cai, L., Xu, Y.: Hierarchical self-supervised augmented knowledge distillation. In: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI), pp. 1217\u20131223 (2021)","DOI":"10.24963\/ijcai.2021\/168"},{"key":"31_CR5","doi-asserted-by":"crossref","unstructured":"Cubuk, E.D., Zoph, B., Shlens, J., Le, Q.V.: Randaugment: practical automated data augmentation with a reduced search space. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 702\u2013703 (2020)","DOI":"10.1109\/CVPRW50498.2020.00359"},{"key":"31_CR6","unstructured":"Das, D., Massa, H., Kulkarni, A., Rekatsinas, T.: An empirical analysis of the impact of data augmentation on distillation (2020)"},{"key":"31_CR7","unstructured":"Dosovitskiy, A., et al.: An image is worth 16$$\\times $$16 words: transformers for image recognition at scale. In: International Conference on Learning Representations (2020)"},{"key":"31_CR8","unstructured":"Fu, J., et al.: Role-wise data augmentation for knowledge distillation. arXiv preprint arXiv:2004.08861 (2020)"},{"issue":"6","key":"31_CR9","doi-asserted-by":"publisher","first-page":"1789","DOI":"10.1007\/s11263-021-01453-z","volume":"129","author":"J Gou","year":"2021","unstructured":"Gou, J., Yu, B., Maybank, S.J., Tao, D.: Knowledge distillation: a survey. Int. J. Comput. Vision 129(6), 1789\u20131819 (2021)","journal-title":"Int. J. Comput. Vision"},{"key":"31_CR10","doi-asserted-by":"crossref","unstructured":"Guo, Q., et al.: Online knowledge distillation via collaborative learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11020\u201311029 (2020)","DOI":"10.1109\/CVPR42600.2020.01103"},{"key":"31_CR11","doi-asserted-by":"crossref","unstructured":"Guo, S.: Dpn: Detail-preserving network with high resolution representation for efficient segmentation of retinal vessels. J. Ambient Intell. Hum. Comput., 1\u201314 (2021)","DOI":"10.1007\/s12652-021-03422-3"},{"key":"31_CR12","unstructured":"Han, J., et al.: You only cut once: boosting data augmentation with a single cut (2022)"},{"key":"31_CR13","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729\u20139738 (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"31_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":"31_CR15","doi-asserted-by":"publisher","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network (2015). https:\/\/doi.org\/10.48550\/ARXIV.1503.02531, https:\/\/arxiv.org\/abs\/1503.02531","DOI":"10.48550\/ARXIV.1503.02531"},{"key":"31_CR16","unstructured":"Ho, D., Liang, E., Chen, X., Stoica, I., Abbeel, P.: Population based augmentation: efficient learning of augmentation policy schedules. In: International Conference on Machine Learning, pp. 2731\u20132741. PMLR (2019)"},{"key":"31_CR17","unstructured":"Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)"},{"key":"31_CR18","first-page":"1","volume":"32","author":"S Lim","year":"2019","unstructured":"Lim, S., Kim, I., Kim, T., Kim, C., Kim, S.: Fast autoaugment. Adv. Neural Inf. Process. Syst. 32, 1\u201311 (2019)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"31_CR19","doi-asserted-by":"crossref","unstructured":"Liu, S., Tian, Y., Chen, T., Shen, L.: Don\u2019t be so dense: sparse-to-sparse gan training without sacrificing performance. Int. J. Comput. Vision 20(X) (2022)","DOI":"10.1007\/s11263-023-01824-8"},{"key":"31_CR20","doi-asserted-by":"crossref","unstructured":"Liu, S., et al.: Paint transformer: feed forward neural painting with stroke prediction. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 6598\u20136607 (2021)","DOI":"10.1109\/ICCV48922.2021.00653"},{"key":"31_CR21","doi-asserted-by":"publisher","first-page":"41799","DOI":"10.1109\/ACCESS.2021.3063692","volume":"9","author":"Z Liu","year":"2021","unstructured":"Liu, Z., Farrell, J., Wandell, B.A.: Isetauto: detecting vehicles with depth and radiance information. IEEE Access 9, 41799\u201341808 (2021)","journal-title":"IEEE Access"},{"key":"31_CR22","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976\u201311986 (2022)","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"31_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1007\/978-3-030-76423-4_3","volume-title":"Reproducible Research in Pattern Recognition","author":"Y Matsubara","year":"2021","unstructured":"Matsubara, Y.: torchdistill: a modular, configuration-driven framework for knowledge distillation. In: Kerautret, B., Colom, M., Kr\u00e4henb\u00fchl, A., Lopresti, D., Monasse, P., Talbot, H. (eds.) RRPR 2021. LNCS, vol. 12636, pp. 24\u201344. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-76423-4_3"},{"key":"31_CR24","doi-asserted-by":"crossref","unstructured":"Park, W., Kim, D., Lu, Y., Cho, M.: Relational knowledge distillation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00409"},{"key":"31_CR25","doi-asserted-by":"crossref","unstructured":"Peng, B., et al.: Correlation congruence for knowledge distillation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5007\u20135016 (2019)","DOI":"10.1109\/ICCV.2019.00511"},{"issue":"1","key":"31_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-021-00894-0","volume":"3","author":"M Razavi","year":"2022","unstructured":"Razavi, M., Alikhani, H., Janfaza, V., Sadeghi, B., Alikhani, E.: An automatic system to monitor the physical distance and face mask wearing of construction workers in covid-19 pandemic. SN Comput. Sci. 3(1), 1\u20138 (2022)","journal-title":"SN Comput. Sci."},{"issue":"3","key":"31_CR27","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211\u2013252 (2015)","journal-title":"Int. J. Comput. Vision"},{"key":"31_CR28","unstructured":"Sharma, S.: Game theory for adversarial attacks and defenses. arXiv preprint arXiv:2110.06166 (2021)"},{"key":"31_CR29","unstructured":"Singh, B., Najibi, M., Davis, L.S.: Sniper: efficient multi-scale training. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol.\u00a031. Curran Associates, Inc. (2018). https:\/\/proceedings.neurips.cc\/paper\/2018\/file\/166cee72e93a992007a89b39eb29628b-Paper.pdf"},{"key":"31_CR30","doi-asserted-by":"crossref","unstructured":"Sun, C., Shrivastava, A., Singh, S., Gupta, A.: Revisiting unreasonable effectiveness of data in deep learning era. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2017)","DOI":"10.1109\/ICCV.2017.97"},{"key":"31_CR31","unstructured":"Tian, Y., Krishnan, D., Isola, P.: Contrastive representation distillation. In: International Conference on Learning Representations (2019)"},{"key":"31_CR32","doi-asserted-by":"crossref","unstructured":"Tung, F., Mori, G.: Similarity-preserving knowledge distillation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1365\u20131374 (2019)","DOI":"10.1109\/ICCV.2019.00145"},{"key":"31_CR33","unstructured":"Wang, H., Lohit, S., Jones, M., Fu, Y.: Knowledge distillation thrives on data augmentation. arXiv preprint arXiv:2012.02909 (2020)"},{"key":"31_CR34","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1007\/978-3-030-92273-3_18","volume-title":"Neural Information Processing","author":"M Wieczorek","year":"2021","unstructured":"Wieczorek, M., Rychalska, B., D\u0105browski, J.: On the\u00a0unreasonable effectiveness of\u00a0centroids in\u00a0image retrieval. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds.) ICONIP 2021. LNCS, vol. 13111, pp. 212\u2013223. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-92273-3_18"},{"key":"31_CR35","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 \u2013 ECCV 2020","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":"31_CR36","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":"31_CR37","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 (ICLR) (2016)"},{"key":"31_CR38","doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., Komodakis, N.: Wide residual networks. In: BMVC (2016)","DOI":"10.5244\/C.30.87"},{"key":"31_CR39","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: beyond empirical risk minimization. In: International Conference on Learning Representations (2018)"},{"key":"31_CR40","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., Sun, J.: Shufflenet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6848\u20136856 (2018)","DOI":"10.1109\/CVPR.2018.00716"},{"key":"31_CR41","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Xiang, T., Hospedales, T.M., Lu, H.: Deep mutual learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00454"},{"key":"31_CR42","doi-asserted-by":"crossref","unstructured":"Zhao, B., Cui, Q., Song, R., Qiu, Y., Liang, J.: Decoupled knowledge distillation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11953\u201311962 (2022)","DOI":"10.1109\/CVPR52688.2022.01165"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ACCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-26284-5_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,7]],"date-time":"2023-12-07T11:19:05Z","timestamp":1701947945000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-26284-5_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031262838","9783031262845"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-26284-5_31","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"23 February 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Macao","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"accv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.accv2022.org","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 Microsoft","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"836","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":"277","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":"33% - 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":"3.3","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":"2.6","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)"}},{"value":"For the ACCV 2022 workshops 25 papers have been accepted from 40 submissions","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}