{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T23:16:04Z","timestamp":1771024564427,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":27,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,8,14]],"date-time":"2022-08-14T00:00:00Z","timestamp":1660435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2040950, 2006889, 2045567,2120240, 2114808, 1725554"],"award-info":[{"award-number":["2040950, 2006889, 2045567,2120240, 2114808, 1725554"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000138","name":"U.S. Department of Education","doi-asserted-by":"publisher","award":["P116S210001, H327S210005, H327S200009"],"award-info":[{"award-number":["P116S210001, H327S210005, H327S200009"]}],"id":[{"id":"10.13039\/100000138","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,8,14]]},"DOI":"10.1145\/3534678.3539297","type":"proceedings-article","created":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T19:06:41Z","timestamp":1660331201000},"page":"1504-1513","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Semi-supervised Drifted Stream Learning with Short Lookback"],"prefix":"10.1145","author":[{"given":"Weijieying","family":"Ren","sequence":"first","affiliation":[{"name":"University of Central Florida, Orlando, FL, USA"}]},{"given":"Pengyang","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Macau, Macau, China"}]},{"given":"Xiaolin","family":"Li","sequence":"additional","affiliation":[{"name":"Nanjing University, Nanjing, China"}]},{"given":"Charles E.","family":"Hughes","sequence":"additional","affiliation":[{"name":"University of Central Florida, Orlando, FL, USA"}]},{"given":"Yanjie","family":"Fu","sequence":"additional","affiliation":[{"name":"University of Central Florida, Orlando, FL, USA"}]}],"member":"320","published-online":{"date-parts":[[2022,8,14]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Mixmatch: A holistic approach to semi-supervised learning. arXiv preprint arXiv:1905.02249","author":"Berthelot David","year":"2019","unstructured":"David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin Raffel. 2019. Mixmatch: A holistic approach to semi-supervised learning. arXiv preprint arXiv:1905.02249 (2019)."},{"key":"e_1_3_2_2_2_1","volume-title":"International Conference on Machine Learning. PMLR.","author":"Chrysakis Aristotelis","year":"2020","unstructured":"Aristotelis Chrysakis and Marie-Francine Moens. 2020. Online continual learning from imbalanced data. In International Conference on Machine Learning. PMLR."},{"key":"e_1_3_2_2_3_1","volume-title":"Advances in Neural Information Processing Systems","volume":"34","author":"Deng Danruo","year":"2021","unstructured":"Danruo Deng, Guangyong Chen, Jianye Hao, Qiong Wang, and Pheng-Ann Heng. 2021. Flattening Sharpness for Dynamic Gradient Projection Memory Benefits Continual Learning. Advances in Neural Information Processing Systems, Vol. 34 (2021)."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.368"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403214"},{"key":"e_1_3_2_2_6_1","volume-title":"International Conference on Learning Representations","author":"Laine Samuli","year":"2017","unstructured":"Samuli Laine and Timo Aila. 2017. Temporal ensembling for semi-supervised learning. International Conference on Learning Representations (2017)."},{"key":"e_1_3_2_2_7_1","volume-title":"Workshop on challenges in representation learning, ICML","volume":"3","author":"Dong-Hyun","unstructured":"Dong-Hyun Lee et almbox. 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. 896."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00116"},{"key":"e_1_3_2_2_9_1","first-page":"22338","article-title":"a. Learning to adapt to evolving domains","volume":"33","author":"Liu Hong","year":"2020","unstructured":"Hong Liu, Mingsheng Long, Jianmin Wang, and Yu Wang. 2020 a. Learning to adapt to evolving domains. Advances in Neural Information Processing Systems, Vol. 33 (2020), 22338--22348.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_10_1","volume-title":"International Conference on Machine Learning","volume":"2","author":"Liu Weiyang","year":"2016","unstructured":"Weiyang Liu, Yandong Wen, Zhiding Yu, and Meng Yang. 2016. Large-margin softmax loss for convolutional neural networks.. In International Conference on Machine Learning, Vol. 2. 7."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01226"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2019.03.010"},{"key":"e_1_3_2_2_13_1","volume-title":"Virtual adversarial training: a regularization method for supervised and semi-supervised learning","author":"Miyato Takeru","year":"2018","unstructured":"Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii. 2018. Virtual adversarial training: a regularization method for supervised and semi-supervised learning. IEEE transactions on pattern analysis and machine intelligence (2018)."},{"key":"e_1_3_2_2_14_1","volume-title":"Exploring generalization in deep learning. Advances in neural information processing systems","author":"Neyshabur Behnam","year":"2017","unstructured":"Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, and Nati Srebro. 2017. Exploring generalization in deep learning. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_2_15_1","volume-title":"Domain adaptation via transfer component analysis","author":"Pan Sinno Jialin","year":"2010","unstructured":"Sinno Jialin Pan, Tsang, James Kwok, and Qiang Yang. 2010. Domain adaptation via transfer component analysis. IEEE transactions on neural networks (2010)."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.eacl-main.131"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2018.00161"},{"key":"e_1_3_2_2_18_1","volume-title":"Advances in Neural Information Processing Systems","volume":"32","author":"Rolnick David","year":"2019","unstructured":"David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy Lillicrap, and Gregory Wayne. 2019. Experience replay for continual learning. Advances in Neural Information Processing Systems, Vol. 32 (2019)."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1137\/0108011"},{"key":"e_1_3_2_2_20_1","volume-title":"International conference on machine learning. PMLR","author":"Schulman John","year":"2015","unstructured":"John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz. 2015. Trust region policy optimization. In International conference on machine learning. PMLR, 1889--1897."},{"key":"e_1_3_2_2_21_1","volume-title":"Overcoming Catastrophic Forgetting in Incremental Few-Shot Learning by Finding Flat Minima. Advances in Neural Information Processing Systems","author":"Shi Guangyuan","year":"2021","unstructured":"Guangyuan Shi, Jiaxin Chen, Wenlong Zhang, Li-Ming Zhan, and Xiao-Ming Wu. 2021. Overcoming Catastrophic Forgetting in Incremental Few-Shot Learning by Finding Flat Minima. Advances in Neural Information Processing Systems (2021)."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611974010.98"},{"key":"e_1_3_2_2_23_1","volume-title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. Advances in neural information processing systems","author":"Tarvainen Antti","year":"2017","unstructured":"Antti Tarvainen and Harri Valpola. 2017. Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.316"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411977"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441720"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3511929"}],"event":{"name":"KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Washington DC USA","acronym":"KDD '22","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3539297","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3534678.3539297","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3534678.3539297","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:59:59Z","timestamp":1750186799000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3539297"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,14]]},"references-count":27,"alternative-id":["10.1145\/3534678.3539297","10.1145\/3534678"],"URL":"https:\/\/doi.org\/10.1145\/3534678.3539297","relation":{},"subject":[],"published":{"date-parts":[[2022,8,14]]},"assertion":[{"value":"2022-08-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}