{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T01:58:15Z","timestamp":1772503095165,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":54,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T00:00:00Z","timestamp":1665360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Australian Research Council Discovery Projects","award":["DP200102940, DP220103044"],"award-info":[{"award-number":["DP200102940, DP220103044"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62141209, 61932007, 61972013"],"award-info":[{"award-number":["62141209, 61932007, 61972013"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,10,10]]},"DOI":"10.1145\/3551349.3561153","type":"proceedings-article","created":{"date-parts":[[2023,1,5]],"date-time":"2023-01-05T20:43:54Z","timestamp":1672951434000},"page":"1-12","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Patching Weak Convolutional Neural Network Models through Modularization and Composition"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0828-5544","authenticated-orcid":false,"given":"Binhang","family":"Qi","sequence":"first","affiliation":[{"name":"SKLSDE Lab, Beihang University, China and Beijing Advanced Innovation Center for Big Data and Brain Computing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7654-5574","authenticated-orcid":false,"given":"Hailong","family":"Sun","sequence":"additional","affiliation":[{"name":"SKLSDE Lab, Beihang University, China and Beijing Advanced Innovation Center for Big Data and Brain Computing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9895-4600","authenticated-orcid":false,"given":"Xiang","family":"Gao","sequence":"additional","affiliation":[{"name":"SKLSDE Lab, Beihang University, China and Beijing Advanced Innovation Center for Big Data and Brain Computing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3063-9425","authenticated-orcid":false,"given":"Hongyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"The University of Newcastle, Australia"}]}],"member":"320","published-online":{"date-parts":[[2023,1,5]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"International Conference on Neural Information Processing Systems. 2180\u20132188","author":"Chen Xi","year":"2016","unstructured":"Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel. 2016. Infogan: Interpretable representation learning by information maximizing generative adversarial nets. In International Conference on Neural Information Processing Systems. 2180\u20132188."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Cyril\u00a0W Cleverdon. 1972. On the inverse relationship of recall and precision. Journal of documentation(1972).","DOI":"10.1108\/eb026538"},{"key":"e_1_3_2_1_3_1","volume-title":"International Conference on Language Resources and Evaluation. 261\u2013266","author":"Derczynski Leon","year":"2016","unstructured":"Leon Derczynski. 2016. Complementarity, F-score, and NLP Evaluation. In International Conference on Language Resources and Evaluation. 261\u2013266."},{"key":"e_1_3_2_1_4_1","volume-title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Association for Computational Linguistics, 4171\u20134186."},{"key":"e_1_3_2_1_5_1","first-page":"1","article-title":"Neural architecture search: A survey.J","volume":"20","author":"Elsken Thomas","year":"2019","unstructured":"Thomas Elsken, Jan\u00a0Hendrik Metzen, Frank Hutter, 2019. Neural architecture search: A survey.J. Mach. Learn. Res. 20, 55 (2019), 1\u201321.","journal-title":"Mach. Learn. Res."},{"key":"e_1_3_2_1_6_1","unstructured":"FAIR. 2022. fvcore. https:\/\/github.com\/facebookresearch\/fvcore"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3395363.3397357"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3377811.3380415"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.81"},{"key":"e_1_3_2_1_10_1","volume-title":"Deep learning","author":"Goodfellow Ian","unstructured":"Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep learning. MIT press."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0950-5849(01)00189-6"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_13_1","volume-title":"A genetic algorithm for function optimization: a Matlab implementation. Ncsu-ie tr 95, 09","author":"Houck R","year":"1995","unstructured":"Christopher\u00a0R Houck, Jeff Joines, and Michael\u00a0G Kay. 1995. A genetic algorithm for function optimization: a Matlab implementation. Ncsu-ie tr 95, 09 (1995), 1\u201310."},{"key":"e_1_3_2_1_14_1","unstructured":"Hiroaki Kingetsu Kenichi Kobayashi and Taiji Suzuki. 2021. Neural Network Module Decomposition and Recomposition. arXiv preprint arXiv:2112.13208(2021)."},{"key":"e_1_3_2_1_15_1","volume-title":"Modular networks: Learning to decompose neural computation. Advances in neural information processing systems 31","author":"Kirsch Louis","year":"2018","unstructured":"Louis Kirsch, Julius Kunze, and David Barber. 2018. Modular networks: Learning to decompose neural computation. Advances in neural information processing systems 31 (2018)."},{"key":"e_1_3_2_1_16_1","unstructured":"Alex Krizhevsky Geoffrey Hinton 2009. Learning multiple layers of features from tiny images. (2009)."},{"key":"e_1_3_2_1_17_1","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky Alex","year":"2012","unstructured":"Alex Krizhevsky, Ilya Sutskever, and Geoffrey\u00a0E Hinton. 2012. Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 25 (2012), 1097\u20131105.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_18_1","unstructured":"Anders Krogh and John\u00a0A Hertz. 1992. A simple weight decay can improve generalization. In Advances in neural information processing systems. 950\u2013957."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_3_2_1_20_1","volume-title":"Pruning Filters for Efficient ConvNets. In International Conference on Learning Representations.","author":"Li Hao","year":"2017","unstructured":"Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans\u00a0Peter Graf. 2017. Pruning Filters for Efficient ConvNets. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2016.2615100"},{"key":"e_1_3_2_1_22_1","unstructured":"Hanxiao Liu Karen Simonyan Oriol Vinyals Chrisantha Fernando and Koray Kavukcuoglu. 2018. Hierarchical Representations for Efficient Architecture Search. In ICLR (Poster). OpenReview.net."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3236024.3236082"},{"key":"e_1_3_2_1_25_1","unstructured":"Yuval Netzer Tao Wang Adam Coates Alessandro Bissacco Bo Wu and Andrew\u00a0Y Ng. 2011. Reading digits in natural images with unsupervised feature learning. (2011)."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3368089.3409668"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3510003.3510051"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132747.3132785"},{"key":"e_1_3_2_1_30_1","unstructured":"Binhang Qi. 2022. CNNSplitter. https:\/\/github.com\/qibinhang\/CNNSplitter"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01191"},{"key":"e_1_3_2_1_32_1","unstructured":"rangeetpan. 2020. Decompose a DNN model into Modules. https:\/\/github.com\/rangeetpan\/decomposeDNNintoModules"},{"key":"e_1_3_2_1_33_1","unstructured":"rangeetpan. 2022. Decompose a CNN model into Modules. https:\/\/github.com\/rangeetpan\/Decomposition"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014780"},{"key":"e_1_3_2_1_35_1","volume-title":"International Conference on Machine Learning. PMLR, 2902\u20132911","author":"Real Esteban","year":"2017","unstructured":"Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka\u00a0Leon Suematsu, Jie Tan, Quoc\u00a0V Le, and Alexey Kurakin. 2017. Large-scale evolution of image classifiers. In International Conference on Machine Learning. PMLR, 2902\u20132911."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1016\/0305-0548(93)E0014-K"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0197-0"},{"key":"e_1_3_2_1_39_1","unstructured":"Karen Simonyan and Andrew Zisserman. 2015. Very Deep Convolutional Networks for Large-Scale Image Recognition. In ICLR."},{"key":"e_1_3_2_1_40_1","volume-title":"Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research 15, 1","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 15, 1 (2014), 1929\u20131958."},{"key":"e_1_3_2_1_41_1","volume-title":"Improving Test Case Generation for REST APIs Through Hierarchical Clustering. In The IEEE\/ACM 36th International Conference on Automated Software Engineering. 117\u2013128","author":"Stallenberg Dimitri","year":"2021","unstructured":"Dimitri Stallenberg, Mitchell Olsthoorn, and Annibale Panichella. 2021. Improving Test Case Generation for REST APIs Through Hierarchical Clustering. In The IEEE\/ACM 36th International Conference on Automated Software Engineering. 117\u2013128."},{"key":"e_1_3_2_1_42_1","volume-title":"International Conference on Machine Learning. 4771\u20134780","author":"Suganuma Masanori","year":"2018","unstructured":"Masanori Suganuma, Mete Ozay, and Takayuki Okatani. 2018. Exploiting the potential of standard convolutional autoencoders for image restoration by evolutionary search. In International Conference on Machine Learning. 4771\u20134780."},{"key":"e_1_3_2_1_43_1","volume-title":"International Conference on Machine Learning. 1139\u20131147","author":"Sutskever Ilya","year":"2013","unstructured":"Ilya Sutskever, James Martens, George Dahl, and Geoffrey Hinton. 2013. On the importance of initialization and momentum in deep learning. In International Conference on Machine Learning. 1139\u20131147."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3180155.3180220"},{"key":"e_1_3_2_1_45_1","unstructured":"tokusumi. 2020. keras-flops. https:\/\/github.com\/tokusumi\/keras-flops"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE43902.2021.00046"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.154"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3293882.3330579"},{"key":"e_1_3_2_1_49_1","volume-title":"Richard Kinh\u00a0Gian Do, and Kaori Togashi","author":"Yamashita Rikiya","year":"2018","unstructured":"Rikiya Yamashita, Mizuho Nishio, Richard Kinh\u00a0Gian Do, and Kaori Togashi. 2018. Convolutional neural networks: an overview and application in radiology. Insights into imaging 9, 4 (2018), 611\u2013629."},{"key":"e_1_3_2_1_50_1","volume-title":"Condconv: Conditionally parameterized convolutions for efficient inference. Advances in Neural Information Processing Systems 32","author":"Yang Brandon","year":"2019","unstructured":"Brandon Yang, Gabriel Bender, Quoc\u00a0V Le, and Jiquan Ngiam. 2019. Condconv: Conditionally parameterized convolutions for efficient inference. Advances in Neural Information Processing Systems 32 (2019)."},{"key":"e_1_3_2_1_51_1","volume-title":"Journal of Physics: Conference Series, Vol.\u00a01168","author":"Ying Xue","year":"2022","unstructured":"Xue Ying. 2019. An overview of overfitting and its solutions. In Journal of Physics: Conference Series, Vol.\u00a01168. IOP Publishing, 022022."},{"key":"e_1_3_2_1_52_1","volume-title":"Wide Residual Networks","author":"Zagoruyko Sergey","unstructured":"Sergey Zagoruyko and Nikos Komodakis. 2016. Wide Residual Networks. In BMVC. BMVA Press."},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/3238147.3238187"},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00257"}],"event":{"name":"ASE '22: 37th IEEE\/ACM International Conference on Automated Software Engineering","location":"Rochester MI USA","acronym":"ASE '22"},"container-title":["Proceedings of the 37th IEEE\/ACM International Conference on Automated Software Engineering"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3551349.3561153","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3551349.3561153","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T08:25:59Z","timestamp":1755851159000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3551349.3561153"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,10]]},"references-count":54,"alternative-id":["10.1145\/3551349.3561153","10.1145\/3551349"],"URL":"https:\/\/doi.org\/10.1145\/3551349.3561153","relation":{},"subject":[],"published":{"date-parts":[[2022,10,10]]},"assertion":[{"value":"2023-01-05","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}