{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T14:17:13Z","timestamp":1756995433401,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":26,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,10,19]],"date-time":"2021-10-19T00:00:00Z","timestamp":1634601600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1866602"],"award-info":[{"award-number":["U1866602"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key R&D Program of China","award":["2019YFB1600700"],"award-info":[{"award-number":["2019YFB1600700"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,10,19]]},"DOI":"10.1145\/3487075.3487171","type":"proceedings-article","created":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T20:35:15Z","timestamp":1638909315000},"page":"1-6","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Online Adversarial Knowledge Distillation for Image Synthesis of Bridge Defect"],"prefix":"10.1145","author":[{"given":"Jiongyu","family":"Guo","sequence":"first","affiliation":[{"name":"Department of Software, ZheJiang University, China"}]},{"given":"Can","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, ZheJiang University, China"}]},{"given":"Yan","family":"Feng","sequence":"additional","affiliation":[{"name":"Department of Computer Science, ZheJiang University, China"}]}],"member":"320","published-online":{"date-parts":[[2021,12,7]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICAwST.2019.8923507"},{"key":"e_1_3_2_1_2_1","first-page":"572","article-title":"A knowledge-based Approach for the Assessment of Damages to Constructions[C]\/\/Proceedings of the 36th CIB W79 2019 Conference. Newcastle","author":"Hamdan A H N","year":"2019","unstructured":"Hamdan A H N , Scherer R J ( 2019 ). A knowledge-based Approach for the Assessment of Damages to Constructions[C]\/\/Proceedings of the 36th CIB W79 2019 Conference. Newcastle , UK. 572 - 582 . Hamdan A H N, Scherer R J (2019). A knowledge-based Approach for the Assessment of Damages to Constructions[C]\/\/Proceedings of the 36th CIB W79 2019 Conference. Newcastle, UK. 572-582.","journal-title":"UK."},{"key":"e_1_3_2_1_3_1","volume-title":"Building an ontological knowledgebase for bridge maintenance. Advances in Engineering Software, 130 (July","author":"Ren G.","year":"2019","unstructured":"Ren , G. , Ding , R. , & Li , H. ( 2019 ). Building an ontological knowledgebase for bridge maintenance. Advances in Engineering Software, 130 (July 2018), 24-40. Ren, G., Ding, R., & Li, H. (2019). Building an ontological knowledgebase for bridge maintenance. Advances in Engineering Software, 130 (July 2018), 24-40."},{"key":"e_1_3_2_1_4_1","volume-title":"NIPS.","author":"Goodfellow J.","year":"2014","unstructured":"I. Goodfellow , J. Pouget-Abadie , M. Mirza , B. Xu , D. WardeFarley , S. Ozair , A. Courville , and Y. Bengio ( 2014 ). Generative Adversarial Networks . In NIPS. I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. WardeFarley, S. Ozair, A. Courville, and Y. Bengio (2014). Generative Adversarial Networks. In NIPS."},{"key":"e_1_3_2_1_5_1","volume-title":"Spectral Normalization for Generative Adversarial Networks[C]\/\/International Conference on Learning Representations","author":"Miyato T","year":"2018","unstructured":"Miyato T , Kataoka T , Koyama M , ( 2018 ). Spectral Normalization for Generative Adversarial Networks[C]\/\/International Conference on Learning Representations . Miyato T, Kataoka T, Koyama M, (2018). Spectral Normalization for Generative Adversarial Networks[C]\/\/International Conference on Learning Representations."},{"key":"e_1_3_2_1_6_1","first-page":"2642","article-title":"Conditional image synthesis with auxiliary classifier gans[C]\/\/International conference on machine learning","author":"Odena A","year":"2017","unstructured":"Odena A , Olah C , Shlens J ( 2017 ). Conditional image synthesis with auxiliary classifier gans[C]\/\/International conference on machine learning . PMLR : 2642 - 2651 . Odena A, Olah C, Shlens J (2017). Conditional image synthesis with auxiliary classifier gans[C]\/\/International conference on machine learning. PMLR: 2642-2651.","journal-title":"PMLR"},{"key":"e_1_3_2_1_7_1","first-page":"214","article-title":"Wasserstein generative adversarial networks[C]\/\/International conference on machine learning","author":"Arjovsky M","year":"2017","unstructured":"Arjovsky M , Chintala S , Bottou L ( 2017 ). Wasserstein generative adversarial networks[C]\/\/International conference on machine learning . PMLR : 214 - 223 . Arjovsky M, Chintala S, Bottou L (2017). Wasserstein generative adversarial networks[C]\/\/International conference on machine learning. PMLR: 214-223.","journal-title":"PMLR"},{"key":"e_1_3_2_1_8_1","volume-title":"Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434","author":"Radford L.","year":"2015","unstructured":"A. Radford , L. Metz , and S. Chintala ( 2015 ). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 . A. Radford, L. Metz, and S. Chintala (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434."},{"key":"e_1_3_2_1_9_1","first-page":"2180","volume-title":"Advances in Neural Information Processing Systems (NIPS)","author":"Chen Y.","year":"2016","unstructured":"X. Chen , Y. Duan , R. Houthooft , J. Schulman , I. Sutskever , and P. Abbeel ( 2016 ). Infogan: Interpretable representation learning by information maximizing generative adversarial nets . In Advances in Neural Information Processing Systems (NIPS) , pages 2172\u2013 2180 . X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel (2016). Infogan: Interpretable representation learning by information maximizing generative adversarial nets. In Advances in Neural Information Processing Systems (NIPS), pages 2172\u20132180."},{"key":"e_1_3_2_1_10_1","first-page":"7354","article-title":"Self-Attention Generative Adversarial Networks. Proceedings of the 36th International Conference on Machine Learning","volume":"97","author":"Zhang H.","year":"2019","unstructured":"Zhang , H. , Goodfellow , I. , Metaxas , D. & Odena , A. ( 2019 ). Self-Attention Generative Adversarial Networks. Proceedings of the 36th International Conference on Machine Learning , in Proceedings of Machine Learning Research 97 : 7354 - 7363 Zhang, H., Goodfellow, I., Metaxas, D. & Odena, A. (2019). Self-Attention Generative Adversarial Networks. Proceedings of the 36th International Conference on Machine Learning, in Proceedings of Machine Learning Research 97:7354-7363","journal-title":"Proceedings of Machine Learning Research"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00454"},{"key":"e_1_3_2_1_13_1","first-page":"2006","article-title":"Feature-map-level online adversarial knowledge distillation[C]\/\/International Conference on Machine Learning","author":"Chung I","year":"2020","unstructured":"Chung I , Park S U , Kim J , ( 2020 ). Feature-map-level online adversarial knowledge distillation[C]\/\/International Conference on Machine Learning . PMLR : 2006 - 2015 . Chung I, Park S U, Kim J, (2020). Feature-map-level online adversarial knowledge distillation[C]\/\/International Conference on Machine Learning. PMLR: 2006-2015.","journal-title":"PMLR"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"crossref","unstructured":"Mundt M Majumder S Murali S (2019). Meta-learning convolutional neural architectures for multi-target concrete defect classification with the concrete defect bridge image dataset[C]\/\/Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 11196-11205.  Mundt M Majumder S Murali S (2019). Meta-learning convolutional neural architectures for multi-target concrete defect classification with the concrete defect bridge image dataset[C]\/\/Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 11196-11205.","DOI":"10.1109\/CVPR.2019.01145"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"crossref","unstructured":"Wen\n      Q Sun\n      L Yang\n      F (\n  2020\n  ). \n  Time series data augmentation for deep learning: A survey[J]\n  . arXiv preprint arXiv:2002.12478.  Wen Q Sun L Yang F (2020). Time series data augmentation for deep learning: A survey[J]. arXiv preprint arXiv:2002.12478.","DOI":"10.24963\/ijcai.2021\/631"},{"key":"e_1_3_2_1_16_1","first-page":"79","article-title":"Enhancing Performance of Deep Learning Models with different Data Augmentation Techniques: A Survey[C]\/\/2020 International Conference on Intelligent Engineering and Management (ICIEM)","author":"Khosla C","year":"2020","unstructured":"Khosla C , Saini B S ( 2020 ). Enhancing Performance of Deep Learning Models with different Data Augmentation Techniques: A Survey[C]\/\/2020 International Conference on Intelligent Engineering and Management (ICIEM) . IEEE , 79 - 85 . Khosla C, Saini B S (2020). Enhancing Performance of Deep Learning Models with different Data Augmentation Techniques: A Survey[C]\/\/2020 International Conference on Intelligent Engineering and Management (ICIEM). IEEE, 79-85.","journal-title":"IEEE"},{"key":"e_1_3_2_1_17_1","volume-title":"Improved Training of Wasserstein GANs[C]\/\/NIPS","author":"Gulrajani I","year":"2017","unstructured":"Gulrajani I , Ahmed F , Arjovsky M , ( 2017 ). Improved Training of Wasserstein GANs[C]\/\/NIPS . Gulrajani I, Ahmed F, Arjovsky M, (2017). Improved Training of Wasserstein GANs[C]\/\/NIPS."},{"key":"e_1_3_2_1_18_1","volume-title":"Defect-GAN: High-fidelity defect synthesis for automated defect inspection[C]\/\/Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision. 2524-2534","author":"Zhang G","year":"2021","unstructured":"Zhang G , Cui K , Hung T Y , ( 2021 ). Defect-GAN: High-fidelity defect synthesis for automated defect inspection[C]\/\/Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision. 2524-2534 . Zhang G, Cui K, Hung T Y, (2021). Defect-GAN: High-fidelity defect synthesis for automated defect inspection[C]\/\/Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision. 2524-2534."},{"key":"e_1_3_2_1_19_1","first-page":"8797","volume-title":"CVPR","author":"Yunjey Choi","year":"2018","unstructured":"Yunjey Choi , Minje Choi, Munyoung Kim , Jung-Woo Ha, Sunghun Kim , and Jaegul Choo ( 2018 ). StarGAN: Unified generative adversarial networks for multi-domain image-to-image translation . In CVPR , pages 8789\u2013 8797 . Yunjey Choi, Minje Choi, Munyoung Kim, Jung-Woo Ha, Sunghun Kim, and Jaegul Choo (2018). StarGAN: Unified generative adversarial networks for multi-domain image-to-image translation. In CVPR, pages 8789\u20138797."},{"key":"e_1_3_2_1_20_1","first-page":"2346","volume-title":"CVPR","author":"Taesung Park","year":"2019","unstructured":"Taesung Park , Ming-Yu Liu, Ting-Chun Wang , and Jun-Yan Zhu ( 2019 ). Semantic image synthesis with spatially-adaptive normalization . In CVPR , pages 2337\u2013 2346 . Taesung Park, Ming-Yu Liu, Ting-Chun Wang, and Jun-Yan Zhu (2019). Semantic image synthesis with spatially-adaptive normalization. In CVPR, pages 2337\u20132346."},{"issue":"7","key":"e_1_3_2_1_21_1","first-page":"38","article-title":"Distilling the Knowledge in a Neural Network[J]","volume":"14","author":"Hinton G","year":"2015","unstructured":"Hinton G , Vinyals O , Dean J ( 2015 ). Distilling the Knowledge in a Neural Network[J] . Computer Science , 14 ( 7 ): 38 - 39 . Hinton G, Vinyals O, Dean J (2015). Distilling the Knowledge in a Neural Network[J]. Computer Science, 14(7):38-39.","journal-title":"Computer Science"},{"key":"e_1_3_2_1_22_1","volume-title":"Cross-layer distillation with semantic calibration[C]\/\/Proceedings of the AAAI Conference on Artificial Intelligence. 35(8): 7028-7036","author":"Chen D","year":"2021","unstructured":"Chen D , Mei J P , Zhang Y , ( 2021 ). Cross-layer distillation with semantic calibration[C]\/\/Proceedings of the AAAI Conference on Artificial Intelligence. 35(8): 7028-7036 . Chen D, Mei J P, Zhang Y, (2021). Cross-layer distillation with semantic calibration[C]\/\/Proceedings of the AAAI Conference on Artificial Intelligence. 35(8): 7028-7036."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5746"},{"key":"e_1_3_2_1_24_1","volume-title":"Be your own teacher: Improve the performance of convolutional neural networks via self distillation[C]\/\/Proceedings of the IEEE\/CVF International Conference on Computer Vision. 3713-3722","author":"Zhang L","year":"2019","unstructured":"Zhang L , Song J , Gao A , ( 2019 ). Be your own teacher: Improve the performance of convolutional neural networks via self distillation[C]\/\/Proceedings of the IEEE\/CVF International Conference on Computer Vision. 3713-3722 . Zhang L, Song J, Gao A, (2019). Be your own teacher: Improve the performance of convolutional neural networks via self distillation[C]\/\/Proceedings of the IEEE\/CVF International Conference on Computer Vision. 3713-3722."},{"key":"e_1_3_2_1_25_1","volume-title":"Self-knowledge distillation: A simple way for better generalization. arXiv preprint arXiv:2006.12000","author":"Kyungyul Kim","year":"2020","unstructured":"Kyungyul Kim , ByeongMoon Ji, Doyoung Yoon , and Sangheum Hwang ( 2020 ). Self-knowledge distillation: A simple way for better generalization. arXiv preprint arXiv:2006.12000 (2020). Kyungyul Kim, ByeongMoon Ji, Doyoung Yoon, and Sangheum Hwang (2020). Self-knowledge distillation: A simple way for better generalization. arXiv preprint arXiv:2006.12000 (2020)."},{"key":"e_1_3_2_1_26_1","volume-title":"A note on the inception score[J]. arXiv preprint arXiv:1801.01973","author":"Barratt S","year":"2018","unstructured":"Barratt S , Sharma R ( 2018 ). A note on the inception score[J]. arXiv preprint arXiv:1801.01973 . Barratt S, Sharma R (2018). A note on the inception score[J]. arXiv preprint arXiv:1801.01973."}],"event":{"name":"CSAE 2021: The 5th International Conference on Computer Science and Application Engineering","acronym":"CSAE 2021","location":"Sanya China"},"container-title":["Proceedings of the 5th International Conference on Computer Science and Application Engineering"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3487075.3487171","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3487075.3487171","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:10:11Z","timestamp":1750183811000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3487075.3487171"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,19]]},"references-count":26,"alternative-id":["10.1145\/3487075.3487171","10.1145\/3487075"],"URL":"https:\/\/doi.org\/10.1145\/3487075.3487171","relation":{},"subject":[],"published":{"date-parts":[[2021,10,19]]},"assertion":[{"value":"2021-12-07","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}