{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T10:33:05Z","timestamp":1760524385293,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":46,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T00:00:00Z","timestamp":1658102400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,7,18]]},"DOI":"10.1145\/3533767.3534408","type":"proceedings-article","created":{"date-parts":[[2022,7,15]],"date-time":"2022-07-15T14:28:50Z","timestamp":1657895330000},"page":"227-238","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["HybridRepair: towards annotation-efficient repair for deep learning models"],"prefix":"10.1145","author":[{"given":"Yu","family":"Li","sequence":"first","affiliation":[{"name":"Chinese University of Hong Kong, China"}]},{"given":"Muxi","family":"Chen","sequence":"additional","affiliation":[{"name":"Chinese University of Hong Kong, China"}]},{"given":"Qiang","family":"Xu","sequence":"additional","affiliation":[{"name":"Chinese University of Hong Kong, China"}]}],"member":"320","published-online":{"date-parts":[[2022,7,18]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9207304"},{"key":"e_1_3_2_1_2_1","unstructured":"Jordan T Ash Chicheng Zhang Akshay Krishnamurthy John Langford and Alekh Agarwal. 2019. Deep batch active learning by diverse uncertain gradient lower bounds. arXiv preprint arXiv:1906.03671 https:\/\/openreview.net\/forum?id=ryghZJBKPS \t\t\t\t\t  Jordan T Ash Chicheng Zhang Akshay Krishnamurthy John Langford and Alekh Agarwal. 2019. Deep batch active learning by diverse uncertain gradient lower bounds. arXiv preprint arXiv:1906.03671 https:\/\/openreview.net\/forum?id=ryghZJBKPS"},{"key":"e_1_3_2_1_3_1","volume-title":"MixMatch: A Holistic Approach to Semi-Supervised Learning. Advances in Neural Information Processing Systems, 32","author":"Berthelot David","year":"2019","unstructured":"David Berthelot , Nicholas Carlini , Ian Goodfellow , Nicolas Papernot , Avital Oliver , and Colin A Raffel . 2019. MixMatch: A Holistic Approach to Semi-Supervised Learning. Advances in Neural Information Processing Systems, 32 ( 2019 ), https:\/\/proceedings.neurips.cc\/paper\/2019\/hash\/1cd138d0499a68f4bb72bee04bbec2d7-Abstract.html David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin A Raffel. 2019. MixMatch: A Holistic Approach to Semi-Supervised Learning. Advances in Neural Information Processing Systems, 32 (2019), https:\/\/proceedings.neurips.cc\/paper\/2019\/hash\/1cd138d0499a68f4bb72bee04bbec2d7-Abstract.html"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00946"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01549"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Xinlei Chen Saining Xie and Kaiming He. 2021. An empirical study of training self-supervised vision transformers. arXiv preprint arXiv:2104.02057 https:\/\/doi.org\/10.48550\/arXiv.2104.02057 \t\t\t\t\t  Xinlei Chen Saining Xie and Kaiming He. 2021. An empirical study of training self-supervised vision transformers. arXiv preprint arXiv:2104.02057 https:\/\/doi.org\/10.48550\/arXiv.2104.02057","DOI":"10.1109\/ICCV48922.2021.00950"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.231"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3395363.3397357"},{"key":"e_1_3_2_1_9_1","volume-title":"Sound and Complete Neural Network Repair with Minimality and Locality Guarantees. ICLR 2022 arXiv:2110","author":"Fu Feisi","year":"2021","unstructured":"Feisi Fu and Wenchao Li . 2021 . Sound and Complete Neural Network Repair with Minimality and Locality Guarantees. ICLR 2022 arXiv:2110 .07682, arxiv:2110.07682 Feisi Fu and Wenchao Li. 2021. Sound and Complete Neural Network Repair with Minimality and Locality Guarantees. ICLR 2022 arXiv:2110.07682, arxiv:2110.07682"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58607-2_30"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.29007\/699q"},{"volume-title":"Deep learning","author":"Goodfellow Ian","key":"e_1_3_2_1_12_1","unstructured":"Ian Goodfellow , Yoshua Bengio , and Aaron Courville . 2016. Deep learning . MIT press . http:\/\/www.deeplearningbook.org Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep learning. MIT press. http:\/\/www.deeplearningbook.org"},{"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","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2013.6706807"},{"key":"e_1_3_2_1_15_1","volume-title":"Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861, https:\/\/doi.org\/10.48550\/arXiv.1704.04861","author":"Howard Andrew G","year":"2017","unstructured":"Andrew G Howard , Menglong Zhu , Bo Chen , Dmitry Kalenichenko , Weijun Wang , Tobias Weyand , Marco Andreetto , and Hartwig Adam . 2017 . Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861, https:\/\/doi.org\/10.48550\/arXiv.1704.04861 Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. 2017. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861, https:\/\/doi.org\/10.48550\/arXiv.1704.04861"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2019.00108"},{"key":"e_1_3_2_1_17_1","unstructured":"Alex Krizhevsky and Geoffrey Hinton. 2009. Learning multiple layers of features from tiny images. Citeseer. \t\t\t\t\t  Alex Krizhevsky and Geoffrey Hinton. 2009. Learning multiple layers of features from tiny images. Citeseer."},{"key":"e_1_3_2_1_18_1","unstructured":"Samuli Laine and Timo Aila. 2016. Temporal ensembling for semi-supervised learning. arXiv preprint arXiv:1610.02242 https:\/\/doi.org\/10.48550\/arXiv.1610.02242 \t\t\t\t\t  Samuli Laine and Timo Aila. 2016. Temporal ensembling for semi-supervised learning. arXiv preprint arXiv:1610.02242 https:\/\/doi.org\/10.48550\/arXiv.1610.02242"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2020.106368"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3236024.3236082"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.21105\/joss.00205"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2017.127"},{"key":"e_1_3_2_1_24_1","unstructured":"Yuval Netzer T. Wang A. Coates A. Bissacco B. Wu and A. Ng. 2011. Reading Digits in Natural Images with Unsupervised Feature Learning. \t\t\t\t\t  Yuval Netzer T. Wang A. Coates A. Bissacco B. Wu and A. Ng. 2011. Reading Digits in Natural Images with Unsupervised Feature Learning."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132747.3132785"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSME46990.2020.00079"},{"key":"e_1_3_2_1_27_1","volume-title":"Schwing","author":"Zhongzheng","year":"2020","unstructured":"Zhongzheng Ren^\u2217, Raymond A. Yeh^\u2217, and Alexander G . Schwing . 2020 . Not All Unlabeled Data are Equal : Learning to Weight Data in Semi-supervised Learning. In Neural Information Processing Systems (NeurIPS) . https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/f7ac67a9aa8d255282de7d11391e1b69-Abstract.html ^\u2217 equal contribution Zhongzheng Ren^\u2217, Raymond A. Yeh^\u2217, and Alexander G. Schwing. 2020. Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning. In Neural Information Processing Systems (NeurIPS). https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/f7ac67a9aa8d255282de7d11391e1b69-Abstract.html ^\u2217 equal contribution"},{"key":"e_1_3_2_1_28_1","volume-title":"Regularization with stochastic transformations and perturbations for deep semi-supervised learning. Advances in neural information processing systems, 29","author":"Sajjadi Mehdi","year":"2016","unstructured":"Mehdi Sajjadi , Mehran Javanmardi , and Tolga Tasdizen . 2016. Regularization with stochastic transformations and perturbations for deep semi-supervised learning. Advances in neural information processing systems, 29 ( 2016 ), 1163\u20131171. https:\/\/proceedings.neurips.cc\/paper\/2016\/hash\/30ef30b64204a3088a26bc2e6ecf7602-Abstract.html Mehdi Sajjadi, Mehran Javanmardi, and Tolga Tasdizen. 2016. Regularization with stochastic transformations and perturbations for deep semi-supervised learning. Advances in neural information processing systems, 29 (2016), 1163\u20131171. https:\/\/proceedings.neurips.cc\/paper\/2016\/hash\/30ef30b64204a3088a26bc2e6ecf7602-Abstract.html"},{"key":"e_1_3_2_1_29_1","volume-title":"Active Learning for Convolutional Neural Networks: A Core-Set Approach. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=H1aIuk-RW","author":"Sener Ozan","year":"2018","unstructured":"Ozan Sener and Silvio Savarese . 2018 . Active Learning for Convolutional Neural Networks: A Core-Set Approach. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=H1aIuk-RW Ozan Sener and Silvio Savarese. 2018. Active Learning for Convolutional Neural Networks: A Core-Set Approach. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=H1aIuk-RW"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3324884.3416621"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR48806.2021.9412716"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00607"},{"key":"e_1_3_2_1_33_1","unstructured":"Kihyuk Sohn David Berthelot Chun-Liang Li Zizhao Zhang Nicholas Carlini Ekin D Cubuk Alex Kurakin Han Zhang and Colin Raffel. 2020. Fixmatch: Simplifying semi-supervised learning with consistency and confidence. arXiv preprint arXiv:2001.07685 https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/06964dce9addb1c5cb5d6e3d9838f733-Abstract.html \t\t\t\t\t  Kihyuk Sohn David Berthelot Chun-Liang Li Zizhao Zhang Nicholas Carlini Ekin D Cubuk Alex Kurakin Han Zhang and Colin Raffel. 2020. Fixmatch: Simplifying semi-supervised learning with consistency and confidence. arXiv preprint arXiv:2001.07685 https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/06964dce9addb1c5cb5d6e3d9838f733-Abstract.html"},{"key":"e_1_3_2_1_34_1","unstructured":"Shuang Song David Berthelot and Afshin Rostamizadeh. 2019. Combining mixmatch and active learning for better accuracy with fewer labels. arXiv preprint arXiv:1912.00594 arxiv:1912.00594 \t\t\t\t\t  Shuang Song David Berthelot and Afshin Rostamizadeh. 2019. Combining mixmatch and active learning for better accuracy with fewer labels. arXiv preprint arXiv:1912.00594 arxiv:1912.00594"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3453483.3454064"},{"key":"e_1_3_2_1_36_1","unstructured":"Youcheng Sun Xiaowei Huang Daniel Kroening James Sharp Matthew Hill and Rob Ashmore. 2018. Testing deep neural networks. arXiv preprint arXiv:1803.04792 arxiv:1803.04792 \t\t\t\t\t  Youcheng Sun Xiaowei Huang Daniel Kroening James Sharp Matthew Hill and Rob Ashmore. 2018. Testing deep neural networks. arXiv preprint arXiv:1803.04792 arxiv:1803.04792"},{"key":"e_1_3_2_1_37_1","unstructured":"Ilya Sutskever Oriol Vinyals and Quoc V Le. 2014. Sequence to sequence learning with neural networks. In Advances in neural information processing systems. 3104\u20133112. https:\/\/proceedings.neurips.cc\/paper\/2014\/hash\/a14ac55a4f27472c5d894ec1c3c743d2-Abstract.html \t\t\t\t\t  Ilya Sutskever Oriol Vinyals and Quoc V Le. 2014. Sequence to sequence learning with neural networks. In Advances in neural information processing systems. 3104\u20133112. https:\/\/proceedings.neurips.cc\/paper\/2014\/hash\/a14ac55a4f27472c5d894ec1c3c743d2-Abstract.html"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3180155.3180220"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-019-05855-6"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2014.6889457"},{"key":"e_1_3_2_1_41_1","volume-title":"Online adaptation to label distribution shift. Advances in Neural Information Processing Systems, 34","author":"Wu Ruihan","year":"2021","unstructured":"Ruihan Wu , Chuan Guo , Yi Su , and Kilian Q Weinberger . 2021. Online adaptation to label distribution shift. Advances in Neural Information Processing Systems, 34 ( 2021 ), https:\/\/proceedings.neurips.cc\/paper\/2021\/hash\/5e6bd7a6970cd4325e587f02667f7f73-Abstract.html Ruihan Wu, Chuan Guo, Yi Su, and Kilian Q Weinberger. 2021. Online adaptation to label distribution shift. Advances in Neural Information Processing Systems, 34 (2021), https:\/\/proceedings.neurips.cc\/paper\/2021\/hash\/5e6bd7a6970cd4325e587f02667f7f73-Abstract.html"},{"key":"e_1_3_2_1_42_1","volume-title":"International Conference on Machine Learning. 11525\u201311536","author":"Xu Yi","year":"2021","unstructured":"Yi Xu , Lei Shang , Jinxing Ye , Qi Qian , Yu-Feng Li , Baigui Sun , Hao Li , and Rong Jin . 2021 . Dash: Semi-supervised learning with dynamic thresholding . In International Conference on Machine Learning. 11525\u201311536 . http:\/\/proceedings.mlr.press\/v139\/xu21e.html Yi Xu, Lei Shang, Jinxing Ye, Qi Qian, Yu-Feng Li, Baigui Sun, Hao Li, and Rong Jin. 2021. Dash: Semi-supervised learning with dynamic thresholding. In International Conference on Machine Learning. 11525\u201311536. http:\/\/proceedings.mlr.press\/v139\/xu21e.html"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00018"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2021.3096332"},{"key":"e_1_3_2_1_45_1","volume-title":"Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling. Advances in Neural Information Processing Systems, 34","author":"Zhang Bowen","year":"2021","unstructured":"Bowen Zhang , Yidong Wang , Wenxin Hou , Hao Wu , Jindong Wang , Manabu Okumura , and Takahiro Shinozaki . 2021 . Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling. Advances in Neural Information Processing Systems, 34 (2021), https:\/\/proceedings.neurips.cc\/paper\/2021\/hash\/995693c15f439e3d189b06e89d145dd5-Abstract.html Bowen Zhang, Yidong Wang, Wenxin Hou, Hao Wu, Jindong Wang, Manabu Okumura, and Takahiro Shinozaki. 2021. Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling. Advances in Neural Information Processing Systems, 34 (2021), https:\/\/proceedings.neurips.cc\/paper\/2021\/hash\/995693c15f439e3d189b06e89d145dd5-Abstract.html"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/ASE.2019.00043"}],"event":{"name":"ISSTA '22: 31st ACM SIGSOFT International Symposium on Software Testing and Analysis","sponsor":["SIGSOFT ACM Special Interest Group on Software Engineering"],"location":"Virtual South Korea","acronym":"ISSTA '22"},"container-title":["Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3533767.3534408","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3533767.3534408","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T18:43:41Z","timestamp":1750272221000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3533767.3534408"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,18]]},"references-count":46,"alternative-id":["10.1145\/3533767.3534408","10.1145\/3533767"],"URL":"https:\/\/doi.org\/10.1145\/3533767.3534408","relation":{},"subject":[],"published":{"date-parts":[[2022,7,18]]},"assertion":[{"value":"2022-07-18","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}