{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T17:32:58Z","timestamp":1778261578775,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":78,"publisher":"ACM","funder":[{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2023MF007"],"award-info":[{"award-number":["ZR2023MF007"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of China","award":["62372242"],"award-info":[{"award-number":["62372242"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,10,27]]},"DOI":"10.1145\/3746027.3754494","type":"proceedings-article","created":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T06:54:15Z","timestamp":1761375255000},"page":"2556-2565","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Joint Test-time Adaptation with Refined Pseudo-labels and Latent Score Matching"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-6428-8495","authenticated-orcid":false,"given":"Yijie","family":"Yang","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, China and Shandong Key Laboratory of Intelligent Oil &amp; Gas Industrial Software, Qingdao, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9875-9856","authenticated-orcid":false,"given":"Lianyong","family":"Qi","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, China and Shandong Key Laboratory of Intelligent Oil &amp; Gas Industrial Software, Qingdao, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4115-7667","authenticated-orcid":false,"given":"Weiming","family":"Liu","sequence":"additional","affiliation":[{"name":"ByteDance Inc., Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0953-6923","authenticated-orcid":false,"given":"Fan","family":"Wang","sequence":"additional","affiliation":[{"name":"Computer Science and Technology, Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4113-0875","authenticated-orcid":false,"given":"Jing","family":"Du","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of New South Wales, Sydney, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4911-5744","authenticated-orcid":false,"given":"Yuwen","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, China and Shandong Key Laboratory of Intelligent Oil &amp; Gas Industrial Software, Qingdao, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4879-9803","authenticated-orcid":false,"given":"Xiaolong","family":"Xu","sequence":"additional","affiliation":[{"name":"Nanjing University of Information Science and Technology, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4593-1656","authenticated-orcid":false,"given":"Qiang","family":"Ni","sequence":"additional","affiliation":[{"name":"Lancaster University, Lancaster, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4833-2023","authenticated-orcid":false,"given":"Wanchun","family":"Dou","sequence":"additional","affiliation":[{"name":"Nanjing University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3488-4679","authenticated-orcid":false,"given":"Xiaokang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Kansai University, Suita, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,10,27]]},"reference":[{"key":"e_1_3_2_1_1_1","first-page":"1","article-title":"Pseudo-labeling and confirmation bias in deep semi-supervised learning. In 2020 International joint conference on neural networks (IJCNN)","author":"Arazo Eric","year":"2020","unstructured":"Eric Arazo, Diego Ortego, Paul Albert, Noel E O'Connor, and Kevin McGuinness. 2020. Pseudo-labeling and confirmation bias in deep semi-supervised learning. In 2020 International joint conference on neural networks (IJCNN). IEEE, 1-8.","journal-title":"IEEE"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Sid Black Stella Biderman Eric Hallahan Quentin Anthony Leo Gao Laurence Golding Horace He Connor Leahy Kyle McDonell Jason Phang et al. 2022. Gpt-neox-20b: An open-source autoregressive language model. arXiv preprint arXiv:2204.06745 (2022).","DOI":"10.18653\/v1\/2022.bigscience-1.9"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_9"},{"key":"e_1_3_2_1_4_1","volume-title":"Denoising likelihood score matching for conditional score-based data generation. arXiv preprint arXiv:2203.14206","author":"Chao Chen-Hao","year":"2022","unstructured":"Chen-Hao Chao, Wei-Fang Sun, Bo-Wun Cheng, Yi-Chen Lo, Chia-Che Chang, Yu-Lun Liu, Yu-Lin Chang, Chia-Ping Chen, and Chun-Yi Lee. 2022. Denoising likelihood score matching for conditional score-based data generation. arXiv preprint arXiv:2203.14206 (2022)."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00039"},{"key":"e_1_3_2_1_6_1","volume-title":"International conference on machine learning. PMLR, 1597-1607","author":"Chen Ting","year":"2020","unstructured":"Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In International conference on machine learning. PMLR, 1597-1607."},{"key":"e_1_3_2_1_7_1","volume-title":"Robustbench: a standardized adversarial robustness benchmark. arXiv preprint arXiv:2010.09670","author":"Croce Francesco","year":"2020","unstructured":"Francesco Croce, Maksym Andriushchenko, Vikash Sehwag, Edoardo Debenedetti, Nicolas Flammarion, Mung Chiang, Prateek Mittal, and Matthias Hein. 2020. Robustbench: a standardized adversarial robustness benchmark. arXiv preprint arXiv:2010.09670 (2020)."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1142\/S0218488512400247"},{"key":"e_1_3_2_1_9_1","volume-title":"Glm: General language model pretraining with autoregressive blank infilling. arXiv preprint arXiv:2103.10360","author":"Du Zhengxiao","year":"2021","unstructured":"Zhengxiao Du, Yujie Qian, Xiao Liu, Ming Ding, Jiezhong Qiu, Zhilin Yang, and Jie Tang. 2021. Glm: General language model pretraining with autoregressive blank infilling. arXiv preprint arXiv:2103.10360 (2021)."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01134"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3422622"},{"key":"e_1_3_2_1_12_1","volume-title":"International conference on machine learning. PMLR, 1321-1330","author":"Guo Chuan","year":"2017","unstructured":"Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger. 2017. On calibration of modern neural networks. In International conference on machine learning. PMLR, 1321-1330."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_14_1","volume-title":"Benchmarking neural network robustness to common corruptions and perturbations. arXiv preprint arXiv:1903.12261","author":"Hendrycks Dan","year":"2019","unstructured":"Dan Hendrycks and Thomas Dietterich. 2019. Benchmarking neural network robustness to common corruptions and perturbations. arXiv preprint arXiv:1903.12261 (2019)."},{"key":"e_1_3_2_1_15_1","volume-title":"Training products of experts by minimizing contrastive divergence. Neural computation","author":"Hinton Geoffrey E","year":"2002","unstructured":"Geoffrey E Hinton. 2002. Training products of experts by minimizing contrastive divergence. Neural computation, Vol. 14, 8 (2002), 1771-1800."},{"key":"e_1_3_2_1_16_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_17_1","first-page":"2427","article-title":"Test-time classifier adjustment module for model-agnostic domain generalization","volume":"34","author":"Iwasawa Yusuke","year":"2021","unstructured":"Yusuke Iwasawa and Yutaka Matsuo. 2021. Test-time classifier adjustment module for model-agnostic domain generalization. Advances in Neural Information Processing Systems, Vol. 34 (2021), 2427-2440.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_18_1","volume-title":"Test-time adaptation via self-training with nearest neighbor information. arXiv preprint arXiv:2207.10792","author":"Jang Minguk","year":"2022","unstructured":"Minguk Jang, Sae-Young Chung, and Hye Won Chung. 2022. Test-time adaptation via self-training with nearest neighbor information. arXiv preprint arXiv:2207.10792 (2022)."},{"key":"e_1_3_2_1_19_1","volume-title":"A literature survey on domain adaptation of statistical classifiers. URL: http:\/\/sifaka. cs. uiuc. edu\/jiang4\/domainadaptation\/survey","author":"Jiang Jing","year":"2008","unstructured":"Jing Jiang. 2008. A literature survey on domain adaptation of statistical classifiers. URL: http:\/\/sifaka. cs. uiuc. edu\/jiang4\/domainadaptation\/survey, Vol. 3, 1-12 (2008), 3."},{"key":"e_1_3_2_1_20_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_3_2_1_21_1","volume-title":"Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114","author":"Kingma Diederik P","year":"2013","unstructured":"Diederik P Kingma and Max Welling. 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)."},{"key":"e_1_3_2_1_22_1","volume-title":"Sampling multimodal distributions with the vanilla score: Benefits of data-based initialization. arXiv preprint arXiv:2310.01762","author":"Koehler Frederic","year":"2023","unstructured":"Frederic Koehler and Thuy-Duong Vuong. 2023. Sampling multimodal distributions with the vanilla score: Benefits of data-based initialization. arXiv preprint arXiv:2310.01762 (2023)."},{"key":"e_1_3_2_1_23_1","unstructured":"Alex Krizhevsky Geoffrey Hinton et al. 2009. Learning multiple layers of features from tiny images. (2009)."},{"key":"e_1_3_2_1_24_1","volume-title":"International conference on machine learning. PMLR, 11710-11728","author":"Kundu Jogendra Nath","year":"2022","unstructured":"Jogendra Nath Kundu, Akshay R Kulkarni, Suvaansh Bhambri, Deepesh Mehta, Shreyas Anand Kulkarni, Varun Jampani, and Venkatesh Babu Radhakrishnan. 2022. Balancing discriminability and transferability for source-free domain adaptation. In International conference on machine learning. PMLR, 11710-11728."},{"key":"e_1_3_2_1_25_1","volume-title":"Tiny imagenet visual recognition challenge. CS 231N","author":"Le Ya","year":"2015","unstructured":"Ya Le and Xuan Yang. 2015. Tiny imagenet visual recognition challenge. CS 231N, Vol. 7, 7 (2015), 3."},{"key":"e_1_3_2_1_26_1","volume-title":"Workshop on challenges in representation learning, ICML","volume":"3","author":"Dong-Hyun","unstructured":"Dong-Hyun Lee et al., 2013. Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. In Workshop on challenges in representation learning, ICML, Vol. 3. Atlanta, 896."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.591"},{"key":"e_1_3_2_1_28_1","volume-title":"The 22nd International Conference on Artificial Intelligence and Statistics. PMLR, 616-625","author":"Li Yitong","year":"2019","unstructured":"Yitong Li, Michael Murias, Samantha Major, Geraldine Dawson, and David Carlson. 2019. On target shift in adversarial domain adaptation. In The 22nd International Conference on Artificial Intelligence and Statistics. PMLR, 616-625."},{"key":"e_1_3_2_1_29_1","volume-title":"A comprehensive survey on test-time adaptation under distribution shifts. arXiv preprint arXiv:2303.15361","author":"Liang Jian","year":"2023","unstructured":"Jian Liang, Ran He, and Tieniu Tan. 2023. A comprehensive survey on test-time adaptation under distribution shifts. arXiv preprint arXiv:2303.15361 (2023)."},{"key":"e_1_3_2_1_30_1","volume-title":"International conference on machine learning. PMLR, 6028-6039","author":"Liang Jian","year":"2020","unstructured":"Jian Liang, Dapeng Hu, and Jiashi Feng. 2020. Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation. In International conference on machine learning. PMLR, 6028-6039."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01636"},{"key":"e_1_3_2_1_32_1","volume-title":"FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models. arXiv preprint arXiv:2506.16218","author":"Liao Xinting","year":"2025","unstructured":"Xinting Liao, Weiming Liu, Jiaming Qian, Pengyang Zhou, Jiahe Xu, Wenjie Wang, Chaochao Chen, Xiaolin Zheng, and Tat-Seng Chua. 2025. FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models. arXiv preprint arXiv:2506.16218 (2025)."},{"key":"e_1_3_2_1_33_1","first-page":"132908","article-title":"Foogd: Federated collaboration for both out-of-distribution generalization and detection","volume":"37","author":"Liao Xinting","year":"2024","unstructured":"Xinting Liao, Weiming Liu, Pengyang Zhou, Fengyuan Yu, Jiahe Xu, Jun Wang, Wenjie Wang, Chaochao Chen, and Xiaolin Zheng. 2024. Foogd: Federated collaboration for both out-of-distribution generalization and detection. Advances in Neural Information Processing Systems, Vol. 37 (2024), 132908-132945.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00738"},{"key":"e_1_3_2_1_35_1","volume-title":"International conference on machine learning. PMLR, 276-284","author":"Liu Qiang","year":"2016","unstructured":"Qiang Liu, Jason Lee, and Michael Jordan. 2016. A kernelized Stein discrepancy for goodness-of-fit tests. In International conference on machine learning. PMLR, 276-284."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3696410.3714860"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52734.2025.00464"},{"key":"e_1_3_2_1_38_1","first-page":"19223","article-title":"Leveraging distribution alignment via stein path for cross-domain cold-start recommendation","volume":"34","author":"Liu Weiming","year":"2021","unstructured":"Weiming Liu, Jiajie Su, Chaochao Chen, and Xiaolin Zheng. 2021b. Leveraging distribution alignment via stein path for cross-domain cold-start recommendation. Advances in Neural Information Processing Systems, Vol. 34 (2021), 19223-19234.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_39_1","volume-title":"Forty-first International Conference on Machine Learning.","author":"Liu Weiming","year":"2024","unstructured":"Weiming Liu, Xiaolin Zheng, Chaochao Chen, Jiahe Xu, Xinting Liao, Fan Wang, Yanchao Tan, and Yew-Soon Ong. 2024. Reducing item discrepancy via differentially private robust embedding alignment for privacy-preserving cross domain recommendation. In Forty-first International Conference on Machine Learning."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512166"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531975"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3233789"},{"key":"e_1_3_2_1_43_1","first-page":"21808","article-title":"Ttt: When does self-supervised test-time training fail or thrive","volume":"34","author":"Liu Yuejiang","year":"2021","unstructured":"Yuejiang Liu, Parth Kothari, Bastien Van Delft, Baptiste Bellot-Gurlet, Taylor Mordan, and Alexandre Alahi. 2021a. Ttt: When does self-supervised test-time training fail or thrive? Advances in Neural Information Processing Systems, Vol. 34 (2021), 21808-21820.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_44_1","volume-title":"Multiscale score matching for out-of-distribution detection. arXiv preprint arXiv:2010.13132","author":"Mahmood Ahsan","year":"2020","unstructured":"Ahsan Mahmood, Junier Oliva, and Martin Styner. 2020. Multiscale score matching for out-of-distribution detection. arXiv preprint arXiv:2010.13132 (2020)."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV57701.2024.00254"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01435"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1002\/int.1018"},{"key":"e_1_3_2_1_48_1","volume-title":"International conference on machine learning. PMLR, 16888-16905","author":"Niu Shuaicheng","year":"2022","unstructured":"Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Yaofo Chen, Shijian Zheng, Peilin Zhao, and Mingkui Tan. 2022. Efficient test-time model adaptation without forgetting. In International conference on machine learning. PMLR, 16888-16905."},{"key":"e_1_3_2_1_49_1","volume-title":"Towards stable test-time adaptation in dynamic wild world. arXiv preprint arXiv:2302.12400","author":"Niu Shuaicheng","year":"2023","unstructured":"Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Zhiquan Wen, Yaofo Chen, Peilin Zhao, and Mingkui Tan. 2023. Towards stable test-time adaptation in dynamic wild world. arXiv preprint arXiv:2302.12400 (2023)."},{"key":"e_1_3_2_1_50_1","volume-title":"Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al., 2019. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, Vol. 32 (2019)."},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"crossref","unstructured":"Joaquin Qui nonero-Candela Sugiyama Masashi and Anton Schwaighofer Neil D Lawrence. 2009. DATASET SHIFT IN MACHINE LEARNING. (2009).","DOI":"10.7551\/mitpress\/9780262170055.001.0001"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/9780262170055.001.0001"},{"key":"e_1_3_2_1_53_1","volume-title":"Universal domain adaptation through self supervision. Advances in neural information processing systems","author":"Saito Kuniaki","year":"2020","unstructured":"Kuniaki Saito, Donghyun Kim, Stan Sclaroff, and Kate Saenko. 2020. Universal domain adaptation through self supervision. Advances in neural information processing systems, Vol. 33 (2020), 16282-16292."},{"key":"e_1_3_2_1_54_1","volume-title":"Improving robustness against common corruptions by covariate shift adaptation. Advances in neural information processing systems","author":"Schneider Steffen","year":"2020","unstructured":"Steffen Schneider, Evgenia Rusak, Luisa Eck, Oliver Bringmann, Wieland Brendel, and Matthias Bethge. 2020. Improving robustness against common corruptions by covariate shift adaptation. Advances in neural information processing systems, Vol. 33 (2020), 11539-11551."},{"key":"e_1_3_2_1_55_1","volume-title":"Alexey Kurakin, and Chun-Liang Li.","author":"Sohn Kihyuk","year":"2020","unstructured":"Kihyuk Sohn, David Berthelot, Nicholas Carlini, Zizhao Zhang, Han Zhang, Colin A Raffel, Ekin Dogus Cubuk, Alexey Kurakin, and Chun-Liang Li. 2020. Fixmatch: Simplifying semi-supervised learning with consistency and confidence. Advances in neural information processing systems, Vol. 33 (2020), 596-608."},{"key":"e_1_3_2_1_56_1","volume-title":"Generative modeling by estimating gradients of the data distribution. Advances in neural information processing systems","author":"Song Yang","year":"2019","unstructured":"Yang Song and Stefano Ermon. 2019. Generative modeling by estimating gradients of the data distribution. Advances in neural information processing systems, Vol. 32 (2019)."},{"key":"e_1_3_2_1_57_1","volume-title":"International conference on machine learning. PMLR, 9229-9248","author":"Sun Yu","year":"2020","unstructured":"Yu Sun, Xiaolong Wang, Zhuang Liu, John Miller, Alexei Efros, and Moritz Hardt. 2020. Test-time training with self-supervision for generalization under distribution shifts. In International conference on machine learning. PMLR, 9229-9248."},{"key":"e_1_3_2_1_58_1","first-page":"18583","article-title":"Measuring robustness to natural distribution shifts in image classification","volume":"33","author":"Taori Rohan","year":"2020","unstructured":"Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht, and Ludwig Schmidt. 2020. Measuring robustness to natural distribution shifts in image classification. Advances in Neural Information Processing Systems, Vol. 33 (2020), 18583-18599.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01948"},{"key":"e_1_3_2_1_60_1","volume-title":"Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971","author":"Touvron Hugo","year":"2023","unstructured":"Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timoth\u00e9e Lacroix, Baptiste Rozi\u00e8re, Naman Goyal, Eric Hambro, Faisal Azhar, et al., 2023. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023)."},{"key":"e_1_3_2_1_61_1","volume-title":"Score-based generative modeling in latent space. Advances in neural information processing systems","author":"Vahdat Arash","year":"2021","unstructured":"Arash Vahdat, Karsten Kreis, and Jan Kautz. 2021. Score-based generative modeling in latent space. Advances in neural information processing systems, Vol. 34 (2021), 11287-11302."},{"key":"e_1_3_2_1_62_1","volume-title":"A connection between score matching and denoising autoencoders. Neural computation","author":"Vincent Pascal","year":"2011","unstructured":"Pascal Vincent. 2011. A connection between score matching and denoising autoencoders. Neural computation, Vol. 23, 7 (2011), 1661-1674."},{"key":"e_1_3_2_1_63_1","volume-title":"On-target Adaptation. CoRR","author":"Wang Dequan","year":"2021","unstructured":"Dequan Wang, Shaoteng Liu, Sayna Ebrahimi, Evan Shelhamer, and Trevor Darrell. 2021. On-target Adaptation. CoRR, Vol. abs\/2109.01087 (2021). arXiv:2109.01087 https:\/\/arxiv.org\/abs\/2109.01087"},{"key":"e_1_3_2_1_64_1","volume-title":"Tent: Fully test-time adaptation by entropy minimization. arXiv preprint arXiv:2006.10726","author":"Wang Dequan","year":"2020","unstructured":"Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, and Trevor Darrell. 2020. Tent: Fully test-time adaptation by entropy minimization. arXiv preprint arXiv:2006.10726 (2020)."},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3672054"},{"key":"e_1_3_2_1_66_1","volume-title":"HCFRec: Hash Collaborative Filtering via Normalized Flow with Structural Consensus for Efficient Recommendation. arXiv preprint arXiv:2205.12042","author":"Wang Fan","year":"2022","unstructured":"Fan Wang, Weiming Liu, Chaochao Chen, Mengying Zhu, and Xiaolin Zheng. 2022b. HCFRec: Hash Collaborative Filtering via Normalized Flow with Structural Consensus for Efficient Recommendation. arXiv preprint arXiv:2205.12042 (2022)."},{"key":"e_1_3_2_1_67_1","volume-title":"Inter-and Intra-Similarity Preserved Counterfactual Incentive Effect Estimation for Recommendation Systems. ACM Transactions on Information Systems","author":"Wang Fan","year":"2025","unstructured":"Fan Wang, Lianyong Qi, Weiming Liu, Bowen Yu, Jintao Chen, and Yanwei Xu. 2025. Inter-and Intra-Similarity Preserved Counterfactual Incentive Effect Estimation for Recommendation Systems. ACM Transactions on Information Systems (2025)."},{"key":"e_1_3_2_1_68_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 S Yu. 2022a. Generalizing to unseen domains: A survey on domain generalization. IEEE transactions on knowledge and data engineering, Vol. 35, 8 (2022), 8052-8072."},{"key":"e_1_3_2_1_69_1","first-page":"681","volume-title":"Proceedings of the 28th international conference on machine learning (ICML-11)","author":"Welling Max","year":"2011","unstructured":"Max Welling and Yee W Teh. 2011. Bayesian learning via stochastic gradient Langevin dynamics. In Proceedings of the 28th international conference on machine learning (ICML-11). Citeseer, 681-688."},{"key":"e_1_3_2_1_70_1","doi-asserted-by":"crossref","unstructured":"Thomas Wolf Lysandre Debut Victor Sanh Julien Chaumond Clement Delangue Anthony Moi Pierric Cistac Tim Rault R\u00e9mi Louf Morgan Funtowicz et al. 2019. Huggingface's transformers: State-of-the-art natural language processing. arXiv preprint arXiv:1910.03771 (2019).","DOI":"10.18653\/v1\/2020.emnlp-demos.6"},{"key":"e_1_3_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01261-8_1"},{"key":"e_1_3_2_1_72_1","volume-title":"Energy-Based Models for Anomaly Detection: A Manifold Diffusion Recovery Approach. In Thirty-seventh Conference on Neural Information Processing Systems.","author":"Yoon Sangwoong","unstructured":"Sangwoong Yoon, Young-Uk Jin, Yung-Kyun Noh, and Frank C. Park. 2023. Energy-Based Models for Anomaly Detection: A Manifold Diffusion Recovery Approach. In Thirty-seventh Conference on Neural Information Processing Systems."},{"key":"e_1_3_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02256"},{"key":"e_1_3_2_1_74_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i15.29600"},{"key":"e_1_3_2_1_75_1","volume-title":"Wide residual networks. arXiv preprint arXiv:1605.07146","author":"Zagoruyko Sergey","year":"2016","unstructured":"Sergey Zagoruyko and Nikos Komodakis. 2016. Wide residual networks. arXiv preprint arXiv:1605.07146 (2016)."},{"key":"e_1_3_2_1_76_1","volume-title":"Delta: degradation-free fully test-time adaptation. arXiv preprint arXiv:2301.13018","author":"Zhao Bowen","year":"2023","unstructured":"Bowen Zhao, Chen Chen, and Shu-Tao Xia. 2023a. Delta: degradation-free fully test-time adaptation. arXiv preprint arXiv:2301.13018 (2023)."},{"key":"e_1_3_2_1_77_1","volume-title":"On Pitfalls of Test-Time Adaptation. In ICLR 2023 Workshop on Pitfalls of limited data and computation for Trustworthy ML.","author":"Zhao Hao","year":"2023","unstructured":"Hao Zhao, Yuejiang Liu, Alexandre Alahi, and Tao Lin. 2023b. On Pitfalls of Test-Time Adaptation. In ICLR 2023 Workshop on Pitfalls of limited data and computation for Trustworthy ML."},{"key":"e_1_3_2_1_78_1","volume-title":"Domain generalization via entropy regularization. Advances in neural information processing systems","author":"Zhao Shanshan","year":"2020","unstructured":"Shanshan Zhao, Mingming Gong, Tongliang Liu, Huan Fu, and Dacheng Tao. 2020. Domain generalization via entropy regularization. Advances in neural information processing systems, Vol. 33 (2020), 16096-16107."}],"event":{"name":"MM '25: The 33rd ACM International Conference on Multimedia","location":"Dublin Ireland","acronym":"MM '25","sponsor":["SIGMM ACM Special Interest Group on Multimedia"]},"container-title":["Proceedings of the 33rd ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3746027.3754494","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T04:01:16Z","timestamp":1765339276000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3746027.3754494"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,27]]},"references-count":78,"alternative-id":["10.1145\/3746027.3754494","10.1145\/3746027"],"URL":"https:\/\/doi.org\/10.1145\/3746027.3754494","relation":{},"subject":[],"published":{"date-parts":[[2025,10,27]]},"assertion":[{"value":"2025-10-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}