{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T15:24:12Z","timestamp":1773156252167,"version":"3.50.1"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030864859","type":"print"},{"value":"9783030864866","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.springernature.com\/gp\/researchers\/text-and-data-mining"},{"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.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-86486-6_34","type":"book-chapter","created":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T15:25:48Z","timestamp":1631201148000},"page":"554-569","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Bridging Few-Shot Learning and\u00a0Adaptation: New Challenges of\u00a0Support-Query Shift"],"prefix":"10.1007","author":[{"given":"Etienne","family":"Bennequin","sequence":"first","affiliation":[]},{"given":"Victor","family":"Bouvier","sequence":"additional","affiliation":[]},{"given":"Myriam","family":"Tami","sequence":"additional","affiliation":[]},{"given":"Antoine","family":"Toubhans","sequence":"additional","affiliation":[]},{"given":"C\u00e9line","family":"Hudelot","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,10]]},"reference":[{"key":"34_CR1","unstructured":"Amodei, D., et al.: Concrete problems in AI safety. arXiv preprint arXiv:1606.06565 (2016)"},{"key":"34_CR2","doi-asserted-by":"crossref","unstructured":"Ben-David, S., et al.: Analysis of representations for domain adaptation. In: Advances in Neural Information Processing Systems, pp. 137\u2013144 (2007)","DOI":"10.7551\/mitpress\/7503.003.0022"},{"key":"34_CR3","unstructured":"Bronskill, J., et al.: Tasknorm: rethinking batch normalization for meta-learning. In ICML, pp. 1153\u20131164. PMLR (2020)"},{"key":"34_CR4","unstructured":"Caldas, S., et al.: Leaf: a benchmark for federated settings. arXiv preprint arXiv:1812.01097 (2018)"},{"key":"34_CR5","unstructured":"Chen, W.-Y., et al.: A closer look at few-shot classification. In: International Conference on Learning Representations (2019)"},{"key":"34_CR6","doi-asserted-by":"crossref","unstructured":"Cohen, G., et al.: EMNIST: extending MNIST to handwritten letters. In: IJCNN. IEEE (2017)","DOI":"10.1109\/IJCNN.2017.7966217"},{"issue":"9","key":"34_CR7","doi-asserted-by":"publisher","first-page":"1853","DOI":"10.1109\/TPAMI.2016.2615921","volume":"39","author":"N Courty","year":"2016","unstructured":"Courty, N., et al.: Optimal transport for domain adaptation. IEEE Trans. Pattern Anal. Mach. Intell. 39(9), 1853\u20131865 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"34_CR8","unstructured":"Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Advances in Neural Information Processing Systems, vol. 26, pp. 2292\u20132300 (2013)"},{"key":"34_CR9","doi-asserted-by":"crossref","unstructured":"Deng, J., et al.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"34_CR10","unstructured":"Dhillon, G.S., et al.: A baseline for few-shot image classification. In: ICLR (2020)"},{"key":"34_CR11","unstructured":"Du, Y., et al.: MetaNorm: learning to normalize few-shot batches across domains. In: International Conference on Learning Representations (2021). https:\/\/openreview.net\/forum?id=9z_dNsC4B5t"},{"key":"34_CR12","unstructured":"Finn, C., et al.: Model-agnostic meta-learning for fast adaptation of deep networks. In: ICML. JMLR (2017)"},{"key":"34_CR13","unstructured":"Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: International Conference on Machine Learning, pp. 1180\u20131189 (2015)"},{"key":"34_CR14","unstructured":"Gulrajani, I., Lopez-Paz, D.: In search of lost domain generalization. In: International Conference on Learning Representations (2021)"},{"key":"34_CR15","doi-asserted-by":"crossref","unstructured":"He, K., et al.: 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_CR16","unstructured":"Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. In: ICLR (2019)"},{"key":"34_CR17","unstructured":"Hu, Y., et al.: Leveraging the feature distribution in transfer-based few-shot learning. arXiv preprint arXiv:2006.03806 (2020)"},{"key":"34_CR18","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: ICML. PMLR (2015)"},{"key":"34_CR19","unstructured":"Krizhevsky, A., et al.: Learning multiple layers of features from tiny images. Citeseer (2009)"},{"key":"34_CR20","unstructured":"Laenen, S., Bertinetto, L.: On episodes, prototypical networks, and few-shot learning. arXiv preprint arXiv:2012.09831 (2020)"},{"key":"34_CR21","unstructured":"Liu, Y., et al.: Learning to propagate labels: transductive propagation network for few-shot learning. In: ICLR (2019)"},{"issue":"10","key":"34_CR22","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2009","unstructured":"Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345\u20131359 (2009)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"5\u20136","key":"34_CR23","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1561\/2200000073","volume":"11","author":"G Peyr\u00e9","year":"2019","unstructured":"Peyr\u00e9, G., et al.: Computational optimal transport: with applications to data science. Found. Trends\u00ae Mach. Learn. 11(5\u20136), 355\u2013607 (2019)","journal-title":"Found. Trends\u00ae Mach. Learn."},{"key":"34_CR24","volume-title":"Dataset Shift in Machine Learning","author":"J Quionero-Candela","year":"2009","unstructured":"Quionero-Candela, J., et al.: Dataset Shift in Machine Learning. The MIT Press, Cambridge (2009)"},{"key":"34_CR25","unstructured":"Ren, M., et al.: Meta-learning for semi-supervised few-shot classification. In: ICLR (2019)"},{"key":"34_CR26","unstructured":"Sahoo, D., et al.: Meta-learning with domain adaptation for few-shot learning under domain shift (2019)"},{"key":"34_CR27","unstructured":"Schneider, S., et al.: Improving robustness against common corruptions by covariate shift adaptation. In: Advances in Neural Information Processing Systems, vol. 33 (2020)"},{"key":"34_CR28","unstructured":"Snell, J., et al.: Prototypical networks for few-shot learning. In: Advances in Neural Information Processing Systems, pp. 4077\u20134087 (2017)"},{"key":"34_CR29","unstructured":"Sun, Y., et al.: Test-time training with self-supervision for generalization under distribution shifts. In: ICML (2020)"},{"key":"34_CR30","unstructured":"Triantafillou, E., et al.: Meta-dataset: a dataset of datasets for learning to learn from few examples. In: ICLR (2020)"},{"key":"34_CR31","unstructured":"Vinyals, O., et al.: Matching networks for one shot learning. In: NIPS (2016)"},{"key":"34_CR32","unstructured":"Wang, D., et al.: Fully test-time adaptation by entropy minimization. In: ICLR (2021)"},{"key":"34_CR33","unstructured":"Zhang, M., et al.: Adaptive risk minimization: a meta-learning approach for tackling group shift. In: ICLR (2021)"},{"key":"34_CR34","unstructured":"Zhao, A., et al.: Domain-adaptive few-shot learning. arXiv preprint arXiv:2003.08626 (2020)"}],"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\/978-3-030-86486-6_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T22:07:17Z","timestamp":1757369237000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86486-6_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030864859","9783030864866"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86486-6_34","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)"}}]}}