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Softw. Eng. Methodol."],"published-print":{"date-parts":[[2023,9,30]]},"abstract":"<jats:p>Over the past few years, deep neural networks (DNNs) have achieved tremendous success and have been continuously applied in many application domains. However, during the practical deployment in industrial tasks, DNNs are found to be erroneous-prone due to various reasons such as overfitting and lacking of robustness to real-world corruptions during practical usage. To address these challenges, many recent attempts have been made to repair DNNs for version updates under practical operational contexts by updating weights (i.e., network parameters) through retraining, fine-tuning, or direct weight fixing at a neural level. Nevertheless, existing solutions often neglect the effects of neural network architecture and weight relationships across neurons and layers. In this work, as the first attempt, we initiate to repair DNNs by jointly optimizing the architecture and weights at a higher (i.e., block level).<\/jats:p>\n          <jats:p>\n            We first perform empirical studies to investigate the limitation of whole network-level and layer-level repairing, which motivates us to explore a novel repairing direction for DNN repair at the block level. To this end, we need to further consider techniques to address two key technical challenges, i.e.,\n            <jats:italic>block localization<\/jats:italic>\n            , where we should localize the targeted block that we need to fix; and how to perform\n            <jats:italic>joint architecture and weight repairing<\/jats:italic>\n            . Specifically, we first propose\n            <jats:italic>adversarial-aware spectrum analysis for vulnerable block localization<\/jats:italic>\n            that considers the neurons\u2019 status and weights\u2019 gradients in blocks during the forward and backward processes, which enables more accurate candidate block localization for repairing even under a few examples. Then, we further propose the\n            <jats:italic>architecture-oriented search-based repairing<\/jats:italic>\n            that relaxes the targeted block to a continuous repairing search space at higher deep feature levels. By jointly optimizing the architecture and weights in that space, we can identify a much better block architecture. We implement our proposed repairing techniques as a tool, named\n            <jats:italic>ArchRepair<\/jats:italic>\n            , and conduct extensive experiments to validate the proposed method. The results show that our method can not only repair but also enhance accuracy and robustness, outperforming the state-of-the-art DNN repair techniques.\n          <\/jats:p>","DOI":"10.1145\/3585005","type":"journal-article","created":{"date-parts":[[2023,7,24]],"date-time":"2023-07-24T12:02:52Z","timestamp":1690200172000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["<i>ArchRepair<\/i>\n            : Block-Level Architecture-Oriented Repairing for Deep Neural Networks"],"prefix":"10.1145","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4302-1915","authenticated-orcid":false,"given":"Hua","family":"Qi","sequence":"first","affiliation":[{"name":"Kyushu University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4559-5426","authenticated-orcid":false,"given":"Zhijie","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Alberta"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0974-9299","authenticated-orcid":false,"given":"Qing","family":"Guo","sequence":"additional","affiliation":[{"name":"Centre for Frontier AI Research (CFAR), A*STAR, and Institute of High Performance Computing (IHPC), A*STAR"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-5956-3417","authenticated-orcid":false,"given":"Jianlang","family":"Chen","sequence":"additional","affiliation":[{"name":"Kyushu University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0857-8611","authenticated-orcid":false,"given":"Felix","family":"Juefei-Xu","sequence":"additional","affiliation":[{"name":"Meta AI"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-6560-5102","authenticated-orcid":false,"given":"Fuyuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Kyushu University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8621-2420","authenticated-orcid":false,"given":"Lei","family":"Ma","sequence":"additional","affiliation":[{"name":"University of Alberta and The University of Tokyo"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8083-4352","authenticated-orcid":false,"given":"Jianjun","family":"Zhao","sequence":"additional","affiliation":[{"name":"Kyushu University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,7,24]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2009.06.035"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/ASE.2011.6100114"},{"key":"e_1_3_2_4_2","volume-title":"International Conference on Learning Representations","author":"Baker Bowen","year":"2017","unstructured":"Bowen Baker, Otkrist Gupta, Nikhil Naik, and Ramesh Raskar. 2017. 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