{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T08:46:50Z","timestamp":1742978810694,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":37,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819609659"},{"type":"electronic","value":"9789819609666"}],"license":[{"start":{"date-parts":[[2024,12,7]],"date-time":"2024-12-07T00:00:00Z","timestamp":1733529600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,7]],"date-time":"2024-12-07T00:00:00Z","timestamp":1733529600000},"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":[[2025]]},"DOI":"10.1007\/978-981-96-0966-6_3","type":"book-chapter","created":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T08:42:25Z","timestamp":1733474545000},"page":"38-54","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Adaptive Bias Discovery for\u00a0Learning Debiased Classifier"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-0593-5670","authenticated-orcid":false,"given":"Jun-Hyun","family":"Bae","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0441-7087","authenticated-orcid":false,"given":"Minho","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3005-2560","authenticated-orcid":false,"given":"Heechul","family":"Jung","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,7]]},"reference":[{"key":"3_CR1","unstructured":"Arjovsky, M., Bottou, L., Gulrajani, I., Lopez-Paz, D.: Invariant risk minimization. arXiv preprint arXiv:1907.02893 (2019)"},{"key":"3_CR2","unstructured":"Arpit, D., Jastrz\u0119bski, S., Ballas, N., Krueger, D., Bengio, E., Kanwal, M.S., Maharaj, T., Fischer, A., Courville, A., Bengio, Y., et\u00a0al.: A closer look at memorization in deep networks. In: International Conference on Machine Learning. pp. 233\u2013242. PMLR (2017)"},{"issue":"2","key":"3_CR3","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1109\/TMI.2018.2867350","volume":"38","author":"P Bandi","year":"2018","unstructured":"Bandi, P., Geessink, O., Manson, Q., Van Dijk, M., Balkenhol, M., Hermsen, M., Bejnordi, B.E., Lee, B., Paeng, K., Zhong, A., et al.: From detection of individual metastases to classification of lymph node status at the patient level: the camelyon17 challenge. IEEE Trans. Med. Imaging 38(2), 550\u2013560 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"3_CR4","unstructured":"Bao, Y., Chang, S., Barzilay, R.: Predict then interpolate: A simple algorithm to learn stable classifiers. In: Meila, M., Zhang, T. (eds.) Proceedings of the 38th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol.\u00a0139, pp. 640\u2013650. PMLR (18\u201324 Jul 2021), https:\/\/proceedings.mlr.press\/v139\/bao21a.html"},{"key":"3_CR5","doi-asserted-by":"crossref","unstructured":"Beery, S., Van\u00a0Horn, G., Perona, P.: Recognition in terra incognita. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 456\u2013473 (2018)","DOI":"10.1007\/978-3-030-01270-0_28"},{"key":"3_CR6","doi-asserted-by":"crossref","unstructured":"Belinkov, Y., Poliak, A., Shieber, S.M., Van\u00a0Durme, B., Rush, A.M.: Don\u2019t take the premise for granted: Mitigating artifacts in natural language inference. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. pp. 877\u2013891 (2019)","DOI":"10.18653\/v1\/P19-1084"},{"key":"3_CR7","doi-asserted-by":"crossref","unstructured":"Belinkov, Y., Poliak, A., Shieber, S.M., Van\u00a0Durme, B., Rush, A.M.: On adversarial removal of hypothesis-only bias in natural language inference. In: Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (* SEM 2019). pp. 256\u2013262 (2019)","DOI":"10.18653\/v1\/S19-1028"},{"issue":"2","key":"3_CR8","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1287\/mnsc.1120.1641","volume":"59","author":"A Ben-Tal","year":"2013","unstructured":"Ben-Tal, A., Den Hertog, D., De Waegenaere, A., Melenberg, B., Rennen, G.: Robust solutions of optimization problems affected by uncertain probabilities. Manage. Sci. 59(2), 341\u2013357 (2013)","journal-title":"Manage. Sci."},{"key":"3_CR9","doi-asserted-by":"crossref","unstructured":"Borkan, D., Dixon, L., Sorensen, J., Thain, N., Vasserman, L.: Nuanced metrics for measuring unintended bias with real data for text classification. In: Companion proceedings of the 2019 world wide web conference. pp. 491\u2013500 (2019)","DOI":"10.1145\/3308560.3317593"},{"key":"3_CR10","doi-asserted-by":"crossref","unstructured":"Christie, G., Fendley, N., Wilson, J., Mukherjee, R.: Functional map of the world. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 6172\u20136180 (2018)","DOI":"10.1109\/CVPR.2018.00646"},{"key":"3_CR11","doi-asserted-by":"crossref","unstructured":"Clark, C., Yatskar, M., Zettlemoyer, L.: Don\u2019t take the easy way out: Ensemble based methods for avoiding known dataset biases. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). pp. 4069\u20134082 (2019)","DOI":"10.18653\/v1\/D19-1418"},{"issue":"3","key":"3_CR12","doi-asserted-by":"publisher","first-page":"946","DOI":"10.1287\/moor.2020.1085","volume":"46","author":"JC Duchi","year":"2021","unstructured":"Duchi, J.C., Glynn, P.W., Namkoong, H.: Statistics of robust optimization: A generalized empirical likelihood approach. Math. Oper. Res. 46(3), 946\u2013969 (2021)","journal-title":"Math. Oper. Res."},{"key":"3_CR13","unstructured":"Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: International Conference on Machine Learning. pp. 1126\u20131135. PMLR (2017)"},{"key":"3_CR14","unstructured":"Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F.A., Brendel, W.: Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. arXiv preprint arXiv:1811.12231 (2018)"},{"key":"3_CR15","doi-asserted-by":"crossref","unstructured":"Gururangan, S., Swayamdipta, S., Levy, O., Schwartz, R., Bowman, S., Smith, N.A.: Annotation artifacts in natural language inference data. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers). pp. 107\u2013112 (2018)","DOI":"10.18653\/v1\/N18-2017"},{"key":"3_CR16","unstructured":"de\u00a0Haan, P., Jayaraman, D., Levine, S.: Causal confusion in imitation learning. In: Advances in Neural Information Processing Systems. pp. 11698\u201311709 (2019)"},{"key":"3_CR17","doi-asserted-by":"crossref","unstructured":"He, H., Zha, S., Wang, H.: Unlearn dataset bias in natural language inference by fitting the residual. In: Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019). pp. 132\u2013142 (2019)","DOI":"10.18653\/v1\/D19-6115"},{"key":"3_CR18","unstructured":"Hu, W., Niu, G., Sato, I., Sugiyama, M.: Does distributionally robust supervised learning give robust classifiers? In: International Conference on Machine Learning. pp. 2029\u20132037. PMLR (2018)"},{"key":"3_CR19","unstructured":"Ilyas, A., Santurkar, S., Tsipras, D., Engstrom, L., Tran, B., Madry, A.: Adversarial examples are not bugs, they are features. In: Advances in Neural Information Processing Systems. pp. 125\u2013136 (2019)"},{"key":"3_CR20","first-page":"18403","volume":"35","author":"N Kim","year":"2022","unstructured":"Kim, N., Hwang, S., Ahn, S., Park, J., Kwak, S.: Learning debiased classifier with biased committee. Adv. Neural. Inf. Process. Syst. 35, 18403\u201318415 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"3_CR21","unstructured":"Koh, P.W., Sagawa, S., Xie, S.M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R.L., Gao, I., Lee, T., et\u00a0al.: Wilds: A benchmark of in-the-wild distribution shifts. In: International Conference on Machine Learning. pp. 5637\u20135664. PMLR (2021)"},{"key":"3_CR22","unstructured":"Liang, W., Zou, J.: Metashift: A dataset of datasets for evaluating contextual distribution shifts and training conflicts. In: International Conference on Learning Representations (2022), https:\/\/openreview.net\/forum?id=MTex8qKavoS"},{"key":"3_CR23","unstructured":"Liu, E.Z., Haghgoo, B., Chen, A.S., Raghunathan, A., Koh, P.W., Sagawa, S., Liang, P., Finn, C.: Just train twice: Improving group robustness without training group information. In: International Conference on Machine Learning. pp. 6781\u20136792. PMLR (2021)"},{"key":"3_CR24","doi-asserted-by":"crossref","unstructured":"Mahabadi, R.K., Belinkov, Y., Henderson, J.: End-to-end bias mitigation by modelling biases in corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. pp. 8706\u20138716 (2020)","DOI":"10.18653\/v1\/2020.acl-main.769"},{"key":"3_CR25","doi-asserted-by":"crossref","unstructured":"McCoy, T., Pavlick, E., Linzen, T.: Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. pp. 3428\u20133448 (2019)","DOI":"10.18653\/v1\/P19-1334"},{"key":"3_CR26","first-page":"20673","volume":"33","author":"J Nam","year":"2020","unstructured":"Nam, J., Cha, H., Ahn, S., Lee, J., Shin, J.: Learning from failure: De-biasing classifier from biased classifier. Adv. Neural. Inf. Process. Syst. 33, 20673\u201320684 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"3_CR27","unstructured":"Piratla, V., Netrapalli, P., Sarawagi, S.: Focus on the common good: Group distributional robustness follows. In: International Conference on Learning Representations (2022), https:\/\/openreview.net\/forum?id=irARV_2VFs4"},{"key":"3_CR28","doi-asserted-by":"crossref","unstructured":"Poliak, A., Naradowsky, J., Haldar, A., Rudinger, R., Van\u00a0Durme, B.: Hypothesis only baselines in natural language inference. In: Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics. pp. 180\u2013191 (2018)","DOI":"10.18653\/v1\/S18-2023"},{"key":"3_CR29","unstructured":"Sagawa*, S., Koh*, P.W., Hashimoto, T.B., Liang, P.: Distributionally robust neural networks. In: International Conference on Learning Representations (2020), https:\/\/openreview.net\/forum?id=ryxGuJrFvS"},{"key":"3_CR30","unstructured":"Sanh, V., Wolf, T., Belinkov, Y., Rush, A.M.: Learning from others\u2019 mistakes: Avoiding dataset biases without modeling them. In: International Conference on Learning Representations (2021), https:\/\/openreview.net\/forum?id=Hf3qXoiNkR"},{"key":"3_CR31","doi-asserted-by":"crossref","unstructured":"Schuster, T., Shah, D., Yeo, Y.J.S., Ortiz, D.R.F., Santus, E., Barzilay, R.: Towards debiasing fact verification models. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). pp. 3419\u20133425 (2019)","DOI":"10.18653\/v1\/D19-1341"},{"key":"3_CR32","unstructured":"Scimeca, L., Oh, S.J., Chun, S., Poli, M., Yun, S.: Which shortcut cues will DNNs choose? a study from the parameter-space perspective. In: International Conference on Learning Representations (2022), https:\/\/openreview.net\/forum?id=qRDQi3ocgR3"},{"key":"3_CR33","doi-asserted-by":"crossref","unstructured":"Sun, B., Saenko, K.: Deep coral: Correlation alignment for deep domain adaptation. In: Computer Vision\u2013ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part III 14. pp. 443\u2013450. Springer (2016)","DOI":"10.1007\/978-3-319-49409-8_35"},{"key":"3_CR34","doi-asserted-by":"crossref","unstructured":"Utama, P.A., Moosavi, N.S., Gurevych, I.: Towards debiasing nlu models from unknown biases. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). pp. 7597\u20137610 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.613"},{"key":"3_CR35","doi-asserted-by":"crossref","unstructured":"Williams, A., Nangia, N., Bowman, S.: A broad-coverage challenge corpus for sentence understanding through inference. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). pp. 1112\u20131122 (2018)","DOI":"10.18653\/v1\/N18-1101"},{"key":"3_CR36","unstructured":"Yao, H., Wang, Y., Li, S., Zhang, L., Liang, W., Zou, J., Finn, C.: Improving out-of-distribution robustness via selective augmentation. In: International Conference on Machine Learning. pp. 25407\u201325437. PMLR (2022)"},{"key":"3_CR37","doi-asserted-by":"publisher","unstructured":"Zellers, R., Holtzman, A., Bisk, Y., Farhadi, A., Choi, Y.: HellaSwag: Can a machine really finish your sentence? In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. pp. 4791\u20134800. Association for Computational Linguistics, Florence, Italy (Jul 2019). https:\/\/doi.org\/10.18653\/v1\/P19-1472, https:\/\/aclanthology.org\/P19-1472","DOI":"10.18653\/v1\/P19-1472"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ACCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-0966-6_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T09:02:12Z","timestamp":1733475732000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-0966-6_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,7]]},"ISBN":["9789819609659","9789819609666"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-0966-6_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,12,7]]},"assertion":[{"value":"7 December 2024","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":"Hanoi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2024","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":"accv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}