{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T01:44:28Z","timestamp":1787017468331,"version":"build-2736575974"},"publisher-location":"New York, NY, USA","reference-count":62,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T00:00:00Z","timestamp":1729468800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"name":"Institute of Information and communications Technology Planning and Evaluation (IITP)","award":["RS-2020-II201373"],"award-info":[{"award-number":["RS-2020-II201373"]}]},{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["No.2018R1A5A7059549"],"award-info":[{"award-number":["No.2018R1A5A7059549"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10,21]]},"DOI":"10.1145\/3627673.3679539","type":"proceedings-article","created":{"date-parts":[[2024,10,20]],"date-time":"2024-10-20T15:34:21Z","timestamp":1729438461000},"page":"1110-1120","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Discrepancy-guided Channel Dropout for Domain Generalization"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-8506-969X","authenticated-orcid":false,"given":"Seonggyeom","family":"Kim","sequence":"first","affiliation":[{"name":"Hanyang University, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4459-0311","authenticated-orcid":false,"given":"Byeongtae","family":"Park","sequence":"additional","affiliation":[{"name":"Hanyang University, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9022-291X","authenticated-orcid":false,"given":"Harim","family":"Lee","sequence":"additional","affiliation":[{"name":"Hanyang University, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5410-6391","authenticated-orcid":false,"given":"Dong-Kyu","family":"Chae","sequence":"additional","affiliation":[{"name":"Hanyang University, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,10,21]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Mohammad Darvishi, Tiago H Falk, and Ioannis Mitliagkas.","author":"Albuquerque Isabela","year":"2019","unstructured":"Isabela Albuquerque, Jo ao Monteiro, Mohammad Darvishi, Tiago H Falk, and Ioannis Mitliagkas. 2019. Generalizing to unseen domains via distribution matching. arXiv preprint arXiv:1911.00804 (2019)."},{"key":"e_1_3_2_1_2_1","first-page":"8265","article-title":"Ensemble of averages: Improving model selection and boosting performance in domain generalization","volume":"35","author":"Arpit Devansh","year":"2022","unstructured":"Devansh Arpit, Huan Wang, Yingbo Zhou, and Caiming Xiong. 2022. Ensemble of averages: Improving model selection and boosting performance in domain generalization. Advances in Neural Information Processing Systems, Vol. 35 (2022), 8265--8277.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01270-0_28"},{"key":"e_1_3_2_1_4_1","volume-title":"A theory of learning from different domains. Machine learning","author":"Ben-David Shai","year":"2010","unstructured":"Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan. 2010. A theory of learning from different domains. Machine learning, Vol. 79 (2010), 151--175."},{"key":"e_1_3_2_1_5_1","volume-title":"Understanding batch normalization. Advances in neural information processing systems","author":"Bjorck Nils","year":"2018","unstructured":"Nils Bjorck, Carla P Gomes, Bart Selman, and Kilian Q Weinberger. 2018. Understanding batch normalization. Advances in neural information processing systems, Vol. 31 (2018)."},{"key":"e_1_3_2_1_6_1","volume-title":"Thirty-seventh Conference on Neural Information Processing Systems. https:\/\/openreview.net\/forum?id=F1mv2L7Rkb","author":"Carvalho Jo","unstructured":"Jo ao B. S. Carvalho, Mengtao Zhang, Robin Geyer, Carlos Cotrini, and Joachim M. Buhmann. 2023. Invariant Anomaly Detection under Distribution Shifts: A Causal Perspective. In Thirty-seventh Conference on Neural Information Processing Systems. https:\/\/openreview.net\/forum?id=F1mv2L7Rkb"},{"key":"e_1_3_2_1_7_1","first-page":"22405","article-title":"Swad: Domain generalization by seeking flat minima","volume":"34","author":"Cha Junbum","year":"2021","unstructured":"Junbum Cha, Sanghyuk Chun, Kyungjae Lee, Han-Cheol Cho, Seunghyun Park, Yunsung Lee, and Sungrae Park. 2021. Swad: Domain generalization by seeking flat minima. Advances in Neural Information Processing Systems, Vol. 34 (2021), 22405--22418.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20050-2_26"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02315"},{"key":"e_1_3_2_1_10_1","volume-title":"Invariant causal mechanisms through distribution matching. arXiv preprint arXiv:2206.11646","author":"Chevalley Mathieu","year":"2022","unstructured":"Mathieu Chevalley, Charlotte Bunne, Andreas Krause, and Stefan Bauer. 2022. Invariant causal mechanisms through distribution matching. arXiv preprint arXiv:2206.11646 (2022)."},{"key":"e_1_3_2_1_11_1","volume-title":"International Conference on Machine Learning. PMLR, 4010--4034","author":"Chu Xu","year":"2022","unstructured":"Xu Chu, Yujie Jin, Wenwu Zhu, Yasha Wang, Xin Wang, Shanghang Zhang, and Hong Mei. 2022. DNA: Domain generalization with diversified neural averaging. In International Conference on Machine Learning. PMLR, 4010--4034."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV57701.2024.00177"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW56347.2022.00461"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01225-0_28"},{"key":"e_1_3_2_1_15_1","volume-title":"Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics. JMLR Workshop and Conference Proceedings, 129--136","author":"David Shai Ben","year":"2010","unstructured":"Shai Ben David, Tyler Lu, Teresa Luu, and D\u00e1vid P\u00e1l. 2010. Impossibility theorems for domain adaptation. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics. JMLR Workshop and Conference Proceedings, 129--136."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.1979.4766909"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_3_2_1_18_1","unstructured":"Alexey Dosovitskiy Lucas Beyer Alexander Kolesnikov Dirk Weissenborn Xiaohua Zhai Thomas Unterthiner Mostafa Dehghani Matthias Minderer Georg Heigold Sylvain Gelly et al. 2020. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-022-01674-w"},{"key":"e_1_3_2_1_20_1","volume-title":"Weighted random sampling with a reservoir. Information processing letters","author":"Efraimidis Pavlos S","year":"2006","unstructured":"Pavlos S Efraimidis and Paul G Spirakis. 2006. Weighted random sampling with a reservoir. Information processing letters, Vol. 97, 5 (2006), 181--185."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.208"},{"key":"e_1_3_2_1_22_1","volume-title":"In Search of Lost Domain Generalization. In International Conference on Learning Representations.","author":"Gulrajani Ishaan","year":"2020","unstructured":"Ishaan Gulrajani and David Lopez-Paz. 2020. In Search of Lost Domain Generalization. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01751"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3565266"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_26_1","volume-title":"Augmix: A simple data processing method to improve robustness and uncertainty. arXiv preprint arXiv:1912.02781","author":"Hendrycks Dan","year":"2019","unstructured":"Dan Hendrycks, Norman Mu, Ekin D Cubuk, Barret Zoph, Justin Gilmer, and Balaji Lakshminarayanan. 2019. Augmix: A simple data processing method to improve robustness and uncertainty. arXiv preprint arXiv:1912.02781 (2019)."},{"key":"e_1_3_2_1_27_1","volume-title":"Self-challenging Improves Cross-Domain Generalization. In European Conference on Computer Vision. 124--140","author":"Huang Zeyi","year":"2020","unstructured":"Zeyi Huang, Haohan Wang, Eric P Xing, and Dong Huang. 2020. Self-challenging Improves Cross-Domain Generalization. In European Conference on Computer Vision. 124--140."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF01908075"},{"key":"e_1_3_2_1_29_1","volume-title":"International conference on machine learning. pmlr, 448--456","author":"Ioffe Sergey","year":"2015","unstructured":"Sergey Ioffe and Christian Szegedy. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning. pmlr, 448--456."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00948"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01133"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.591"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00566"},{"key":"e_1_3_2_1_34_1","volume-title":"Domain generalization for medical imaging classification with linear-dependency regularization. Advances in neural information processing systems","author":"Li Haoliang","year":"2020","unstructured":"Haoliang Li, YuFei Wang, Renjie Wan, Shiqi Wang, Tie-Qiang Li, and Alex Kot. 2020. Domain generalization for medical imaging classification with linear-dependency regularization. Advances in neural information processing systems, Vol. 33 (2020), 3118--3129."},{"key":"e_1_3_2_1_35_1","volume-title":"Uncertainty modeling for out-of-distribution generalization. arXiv preprint arXiv:2202.03958","author":"Li Xiaotong","year":"2022","unstructured":"Xiaotong Li, Yongxing Dai, Yixiao Ge, Jun Liu, Ying Shan, and Ling-Yu Duan. 2022. Uncertainty modeling for out-of-distribution generalization. arXiv preprint arXiv:2202.03958 (2022)."},{"key":"e_1_3_2_1_36_1","volume-title":"Revisiting batch normalization for practical domain adaptation. arXiv preprint arXiv:1603.04779","author":"Li Yanghao","year":"2016","unstructured":"Yanghao Li, Naiyan Wang, Jianping Shi, Jiaying Liu, and Xiaodi Hou. 2016. Revisiting batch normalization for practical domain adaptation. arXiv preprint arXiv:1603.04779 (2016)."},{"key":"e_1_3_2_1_37_1","first-page":"12917","article-title":"Pareto domain adaptation","volume":"34","author":"Liang Jian","year":"2021","unstructured":"Jian Liang, Kaixiong Gong, Shuang Li, Chi Harold Liu, Han Li, Di Liu, Guoren Wang, et al. 2021. Pareto domain adaptation. Advances in Neural Information Processing Systems, Vol. 34 (2021), 12917--12929.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00568"},{"key":"e_1_3_2_1_39_1","volume-title":"International conference on machine learning. PMLR, 10--18","author":"Muandet Krikamol","year":"2013","unstructured":"Krikamol Muandet, David Balduzzi, and Bernhard Sch\u00f6lkopf. 2013. Domain generalization via invariant feature representation. In International conference on machine learning. PMLR, 10--18."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00858"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00936"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01225-0_29"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02312"},{"key":"e_1_3_2_1_44_1","volume-title":"International conference on machine learning. PMLR, 8748--8763","author":"Radford Alec","year":"2021","unstructured":"Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. 2021. Learning transferable visual models from natural language supervision. In International conference on machine learning. PMLR, 8748--8763."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1016\/0377-0427(87)90125-7"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109115"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58542-6_5"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11784"},{"key":"e_1_3_2_1_49_1","volume-title":"International Conference on Machine Learning. PMLR, 8828--8839","author":"Shi Baifeng","year":"2020","unstructured":"Baifeng Shi, Dinghuai Zhang, Qi Dai, Zhanxing Zhu, Yadong Mu, and Jingdong Wang. 2020. Informative dropout for robust representation learning: A shape-bias perspective. In International Conference on Machine Learning. PMLR, 8828--8839."},{"key":"e_1_3_2_1_50_1","volume-title":"Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research","author":"Srivastava Nitish","year":"2014","unstructured":"Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research, Vol. 15, 1 (2014), 1929--1958."},{"key":"e_1_3_2_1_51_1","article-title":"Visualizing data using t-SNE","volume":"9","author":"der Maaten Laurens Van","year":"2008","unstructured":"Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of machine learning research, Vol. 9, 11 (2008).","journal-title":"Journal of machine learning research"},{"key":"e_1_3_2_1_52_1","volume-title":"Principles of risk minimization for learning theory. Advances in neural information processing systems","author":"Vapnik Vladimir","year":"1991","unstructured":"Vladimir Vapnik. 1991. Principles of risk minimization for learning theory. Advances in neural information processing systems, Vol. 4 (1991)."},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.572"},{"key":"e_1_3_2_1_54_1","volume-title":"Generalizing to unseen domains: A survey on domain generalization","author":"Wang Jindong","year":"2022","unstructured":"Jindong Wang, Cuiling Lan, Chang Liu, Yidong Ouyang, Tao Qin, Wang Lu, Yiqiang Chen, Wenjun Zeng, and Philip Yu. 2022. Generalizing to unseen domains: A survey on domain generalization. IEEE Transactions on Knowledge and Data Engineering (2022)."},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00367"},{"key":"e_1_3_2_1_56_1","volume-title":"PGrad: Learning Principal Gradients For Domain Generalization. In The Eleventh International Conference on Learning Representations.","author":"Wang Zhe","year":"2022","unstructured":"Zhe Wang, Jake Grigsby, and Yanjun Qi. 2022. PGrad: Learning Principal Gradients For Domain Generalization. In The Eleventh International Conference on Learning Representations."},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR48806.2021.9412797"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW59228.2023.00508"},{"key":"e_1_3_2_1_59_1","volume-title":"Test-time batch statistics calibration for covariate shift. arXiv preprint arXiv:2110.04065","author":"You Fuming","year":"2021","unstructured":"Fuming You, Jingjing Li, and Zhou Zhao. 2021. Test-time batch statistics calibration for covariate shift. arXiv preprint arXiv:2110.04065 (2021)."},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6967"},{"key":"e_1_3_2_1_61_1","first-page":"5824","article-title":"Gradient surgery for multi-task learning","volume":"33","author":"Yu Tianhe","year":"2020","unstructured":"Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, and Chelsea Finn. 2020. Gradient surgery for multi-task learning. Advances in Neural Information Processing Systems, Vol. 33 (2020), 5824--5836.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_62_1","volume-title":"Domain generalization with mixstyle. arXiv preprint arXiv:2104.02008","author":"Zhou Kaiyang","year":"2021","unstructured":"Kaiyang Zhou, Yongxin Yang, Yu Qiao, and Tao Xiang. 2021. Domain generalization with mixstyle. arXiv preprint arXiv:2104.02008 (2021)."}],"event":{"name":"CIKM '24: The 33rd ACM International Conference on Information and Knowledge Management","location":"Boise ID USA","acronym":"CIKM '24","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 33rd ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3627673.3679539","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3627673.3679539","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T20:58:22Z","timestamp":1750280302000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3627673.3679539"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,21]]},"references-count":62,"alternative-id":["10.1145\/3627673.3679539","10.1145\/3627673"],"URL":"https:\/\/doi.org\/10.1145\/3627673.3679539","relation":{},"subject":[],"published":{"date-parts":[[2024,10,21]]},"assertion":[{"value":"2024-10-21","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}