{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T16:38:48Z","timestamp":1778258328847,"version":"3.51.4"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030865191","type":"print"},{"value":"9783030865207","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-86520-7_16","type":"book-chapter","created":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T15:25:48Z","timestamp":1631201148000},"page":"250-265","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Knowledge Distillation with Distribution Mismatch"],"prefix":"10.1007","author":[{"given":"Dang","family":"Nguyen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sunil","family":"Gupta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Trong","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Santu","family":"Rana","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Phuoc","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Truyen","family":"Tran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ky","family":"Le","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shannon","family":"Ryan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Svetha","family":"Venkatesh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,10]]},"reference":[{"key":"16_CR1","unstructured":"Adriana, R., Nicolas, B., Ebrahimi, S., Antoine, C., Carlo, G., Yoshua, B.: FitNets: hints for thin deep nets. In: ICLR (2015)"},{"key":"16_CR2","doi-asserted-by":"crossref","unstructured":"Ahn, S., Hu, X., Damianou, A., Lawrence, N., Dai, Z.: Variational information distillation for knowledge transfer. In: CVPR, pp. 9163\u20139171 (2019)","DOI":"10.1109\/CVPR.2019.00938"},{"key":"16_CR3","doi-asserted-by":"crossref","unstructured":"Chawla, A., Yin, H., Molchanov, P., Alvarez, J.: Data-free knowledge distillation for object detection. In: CVPR, pp. 3289\u20133298 (2021)","DOI":"10.1109\/WACV48630.2021.00333"},{"key":"16_CR4","unstructured":"Chen, G., Choi, W., Yu, X., Han, T., Chandraker, M.: Learning efficient object detection models with knowledge distillation. In: NIPS, pp. 742\u2013751 (2017)"},{"key":"16_CR5","doi-asserted-by":"crossref","unstructured":"Chen, H., et al.: Data-free learning of student networks. In: ICCV, pp. 3514\u20133522 (2019)","DOI":"10.1109\/ICCV.2019.00361"},{"key":"16_CR6","unstructured":"Eriksson, D., Pearce, M., Gardner, J., Turner, R., Poloczek, M.: Scalable global optimization via local bayesian optimization. In: NIPS, pp. 5496\u20135507 (2019)"},{"key":"16_CR7","unstructured":"Gou, J., Yu, B., Maybank, S.J., Tao, D.: Knowledge distillation: a survey. arXiv preprint arXiv:2006.05525 (2020)"},{"key":"16_CR8","doi-asserted-by":"crossref","unstructured":"Guo, G., Zhang, N.: A survey on deep learning based face recognition. Comput. Vis. Image Underst. 189, 102805 (2019)","DOI":"10.1016\/j.cviu.2019.102805"},{"key":"16_CR9","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)"},{"key":"16_CR10","unstructured":"Kim, J., Park, S., Kwak, N.: Paraphrasing complex network: network compression via factor transfer. In: NIPS, pp. 2760\u20132769 (2018)"},{"key":"16_CR11","unstructured":"Lee, S., Song, B.C.: Graph-based knowledge distillation by multi-head attention network. arXiv preprint arXiv:1907.02226 (2019)"},{"key":"16_CR12","doi-asserted-by":"crossref","unstructured":"Meng, Z., Li, J., Zhao, Y., Gong, Y.: Conditional teacher-student learning. In: ICASSP, pp. 6445\u20136449. IEEE (2019)","DOI":"10.1109\/ICASSP.2019.8683438"},{"key":"16_CR13","doi-asserted-by":"crossref","unstructured":"Nayak, G.K., Mopuri, K.R., Chakraborty, A.: Effectiveness of arbitrary transfer sets for data-free knowledge distillation. In: CVPR, pp. 1430\u20131438 (2021)","DOI":"10.1109\/WACV48630.2021.00147"},{"key":"16_CR14","doi-asserted-by":"crossref","unstructured":"Nguyen, D., Gupta, S., Rana, S., Shilton, A., Venkatesh, S.: Bayesian optimization for categorical and category-specific continuous inputs. In: AAAI, pp. 5256\u20135263 (2020)","DOI":"10.1609\/aaai.v34i04.5971"},{"key":"16_CR15","doi-asserted-by":"crossref","unstructured":"Passalis, N., Tzelepi, M., Tefas, A.: Heterogeneous knowledge distillation using information flow modeling. In: CVPR, pp. 2339\u20132348 (2020)","DOI":"10.1109\/CVPR42600.2020.00241"},{"issue":"5","key":"16_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3234150","volume":"51","author":"S Pouyanfar","year":"2018","unstructured":"Pouyanfar, S., et al.: A survey on deep learning: algorithms, techniques, and applications. ACM Comput. Surv. 51(5), 1\u201336 (2018)","journal-title":"ACM Comput. Surv."},{"key":"16_CR17","unstructured":"Salman, H., Ilyas, A., Engstrom, L., Kapoor, A., Madry, A.: Do adversarially robust ImageNet models transfer better? In: NIPS, pp. 3533\u20133545 (2020)"},{"issue":"1","key":"16_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-48995-4","volume":"9","author":"L Shen","year":"2019","unstructured":"Shen, L., Margolies, L., Rothstein, J., Fluder, E., McBride, R., Sieh, W.: Deep learning to improve breast cancer detection on screening mammography. Sci. Rep. 9(1), 1\u201312 (2019)","journal-title":"Sci. Rep."},{"key":"16_CR19","unstructured":"Snoek, J., Larochelle, H., Adams, R.: Practical Bayesian optimization of machine learning algorithms. In: NIPS, pp. 2951\u20132959 (2012)"},{"key":"16_CR20","unstructured":"Sohn, K., Lee, H., Yan, X.: Learning structured output representation using deep conditional generative models. In: NIPS, pp. 3483\u20133491 (2015)"},{"issue":"1","key":"16_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0212-5","volume":"6","author":"G Sreenu","year":"2019","unstructured":"Sreenu, G., Durai, S.: Intelligent video surveillance: a review through deep learning techniques for crowd analysis. J. Big Data 6(1), 1\u201327 (2019)","journal-title":"J. Big Data"},{"key":"16_CR22","unstructured":"Tian, Y., Krishnan, D., Isola, P.: Contrastive representation distillation. In: ICLR (2020)"},{"key":"16_CR23","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: CVPR, pp. 1498\u20131507 (2020)","DOI":"10.1109\/CVPR42600.2020.00157"},{"key":"16_CR24","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: CVPR, pp. 4133\u20134141 (2017)","DOI":"10.1109\/CVPR.2017.754"},{"issue":"1","key":"16_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3158369","volume":"52","author":"S Zhang","year":"2019","unstructured":"Zhang, S., Yao, L., Sun, A., Tay, Y.: Deep learning based recommender system: a survey and new perspectives. ACM Comput. Surv. 52(1), 1\u201338 (2019)","journal-title":"ACM Comput. Surv."}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases. Research Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86520-7_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T22:05:57Z","timestamp":1757369157000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86520-7_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030865191","9783030865207"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86520-7_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"10 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bilbao","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2021.ecmlpkdd.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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"869","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":"210","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":"24% - 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":"3-4","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":"3-9","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":"The conference was held online due to the COVID-19 pandemic.","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)"}}]}}