{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T00:10:04Z","timestamp":1755821404618,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":29,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,10,26]],"date-time":"2023-10-26T00:00:00Z","timestamp":1698278400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 61832002, 62172094"],"award-info":[{"award-number":["No. 61832002, 62172094"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["No. 2023JBZY033"],"award-info":[{"award-number":["No. 2023JBZY033"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"CCF-Zhipu AI Large Model Foundation"},{"name":"Beijing Natural Science Foundation","award":["No. JQ20023"],"award-info":[{"award-number":["No. JQ20023"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,10,26]]},"DOI":"10.1145\/3581783.3612312","type":"proceedings-article","created":{"date-parts":[[2023,10,27]],"date-time":"2023-10-27T07:26:54Z","timestamp":1698391614000},"page":"1616-1624","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Echoes: Unsupervised Debiasing via Pseudo-bias Labeling in an Echo Chamber"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-7891-1950","authenticated-orcid":false,"given":"Rui","family":"Hu","sequence":"first","affiliation":[{"name":"Beijing Jiaotong University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0023-479X","authenticated-orcid":false,"given":"Yahan","family":"Tu","sequence":"additional","affiliation":[{"name":"China University of Geoscience Beijing, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0699-3205","authenticated-orcid":false,"given":"Jitao","family":"Sang","sequence":"additional","affiliation":[{"name":"Beijing Jiaotong University &amp; Peng Cheng Lab, Beijing; Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,10,27]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"International Conference on Machine Learning. PMLR, 528--539","author":"Bahng Hyojin","year":"2020","unstructured":"Hyojin Bahng, Sanghyuk Chun, Sangdoo Yun, Jaegul Choo, and Seong Joon Oh. 2020. Learning de-biased representations with biased representations. In International Conference on Machine Learning. PMLR, 528--539."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401431"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-020-00257-z"},{"key":"e_1_3_2_1_4_1","volume-title":"7th International Conference on Learning Representations, ICLR 2019","author":"Geirhos Robert","year":"2019","unstructured":"Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A. Wichmann, and Wieland Brendel. 2019. ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019. OpenReview.net. https:\/\/openreview.net\/forum?id=Bygh9j09KX"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00550"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00823"},{"key":"e_1_3_2_1_8_1","first-page":"26449","article-title":"Unbiased classification through bias-contrastive and bias-balanced learning","volume":"34","author":"Hong Youngkyu","year":"2021","unstructured":"Youngkyu Hong and Eunho Yang. 2021. Unbiased classification through bias-contrastive and bias-balanced learning. Advances in Neural Information Processing Systems, Vol. 34 (2021), 26449--26461.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00453"},{"key":"e_1_3_2_1_10_1","volume-title":"Modeling and counteracting exposure bias in recommender systems. arXiv preprint arXiv:2001.04832","author":"Khenissi Sami","year":"2020","unstructured":"Sami Khenissi and Olfa Nasraoui. 2020. Modeling and counteracting exposure bias in recommender systems. arXiv preprint arXiv:2001.04832 (2020)."},{"key":"e_1_3_2_1_11_1","volume-title":"Kingma and Jimmy Ba","author":"Diederik","year":"2015","unstructured":"Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). http:\/\/arxiv.org\/abs\/1412.6980"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2013.77"},{"key":"e_1_3_2_1_13_1","first-page":"25123","article-title":"Learning debiased representation via disentangled feature augmentation","volume":"34","author":"Lee Jungsoo","year":"2021","unstructured":"Jungsoo Lee, Eungyeup Kim, Juyoung Lee, Jihyeon Lee, and Jaegul Choo. 2021. Learning debiased representation via disentangled feature augmentation. Advances in Neural Information Processing Systems, Vol. 34 (2021), 25123--25133.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_14_1","unstructured":"Jungsoo Lee Jeonghoon Park Daeyoung Kim Juyoung Lee Edward Choi and Jaegul Choo. 2023. BiasEnsemble: Revisiting the Importance of Amplifying Bias for Debiasing. (2023)."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01922"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19778-9_16"},{"key":"e_1_3_2_1_17_1","volume-title":"International Conference on Machine Learning. PMLR, 6781--6792","author":"Liu Evan Z","year":"2021","unstructured":"Evan Z Liu, Behzad Haghgoo, Annie S Chen, Aditi Raghunathan, Pang Wei Koh, Shiori Sagawa, Percy Liang, and Chelsea Finn. 2021. Just train twice: Improving group robustness without training group information. In International Conference on Machine Learning. PMLR, 6781--6792."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.425"},{"key":"e_1_3_2_1_19_1","first-page":"20673","article-title":"Learning from failure: De-biasing classifier from biased classifier","volume":"33","author":"Nam Junhyun","year":"2020","unstructured":"Junhyun Nam, Hyuntak Cha, Sungsoo Ahn, Jaeho Lee, and Jinwoo Shin. 2020. Learning from failure: De-biasing classifier from biased classifier. Advances in Neural Information Processing Systems, Vol. 33 (2020), 20673--20684.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/2566486.2568012"},{"key":"e_1_3_2_1_21_1","volume-title":"International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=ryxGuJrFvS","author":"Shiori","year":"2020","unstructured":"Shiori Sagawa*, Pang Wei Koh*, Tatsunori B. Hashimoto, and Percy Liang. 2020. Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=ryxGuJrFvS"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01624"},{"key":"e_1_3_2_1_23_1","first-page":"9573","article-title":"The pitfalls of simplicity bias in neural networks","volume":"33","author":"Shah Harshay","year":"2020","unstructured":"Harshay Shah, Kaustav Tamuly, Aditi Raghunathan, Prateek Jain, and Praneeth Netrapalli. 2020. The pitfalls of simplicity bias in neural networks. Advances in Neural Information Processing Systems, Vol. 33 (2020), 9573--9585.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData55660.2022.10020444"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01330"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01626"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00365-006-0663-2"},{"key":"e_1_3_2_1_28_1","volume-title":"Sabuncu","author":"Zhang Zhilu","year":"2018","unstructured":"Zhilu Zhang and Mert R. Sabuncu. 2018. Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels. In Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montr\u00e9 al, Canada, Samy Bengio, Hanna M. Wallach, Hugo Larochelle, Kristen Grauman, Nicol\u00f2 Cesa-Bianchi, and Roman Garnett (Eds.). 8792--8802. https:\/\/proceedings.neurips.cc\/paper\/2018\/hash\/f2925f97bc13ad2852a7a551802feea0-Abstract.html"},{"key":"e_1_3_2_1_29_1","volume-title":"Places: A 10 million image database for scene recognition","author":"Zhou Bolei","year":"2017","unstructured":"Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba. 2017. Places: A 10 million image database for scene recognition. IEEE transactions on pattern analysis and machine intelligence, Vol. 40, 6 (2017), 1452--1464."}],"event":{"name":"MM '23: The 31st ACM International Conference on Multimedia","sponsor":["SIGMM ACM Special Interest Group on Multimedia"],"location":"Ottawa ON Canada","acronym":"MM '23"},"container-title":["Proceedings of the 31st ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3581783.3612312","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3581783.3612312","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T23:56:40Z","timestamp":1755820600000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3581783.3612312"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,26]]},"references-count":29,"alternative-id":["10.1145\/3581783.3612312","10.1145\/3581783"],"URL":"https:\/\/doi.org\/10.1145\/3581783.3612312","relation":{},"subject":[],"published":{"date-parts":[[2023,10,26]]},"assertion":[{"value":"2023-10-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}