{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T22:59:55Z","timestamp":1783983595607,"version":"3.55.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,8]]},"abstract":"<jats:p>Adversarial training is one of the most effective approaches for deep learning models to defend against adversarial examples.\n\nUnlike other defense strategies, adversarial training aims to enhance the robustness of models intrinsically.\n\nDuring the past few years, adversarial training has been studied and discussed from various aspects, which deserves a comprehensive review.\n\nFor the first time in this survey, we systematically review the recent progress on adversarial training for adversarial robustness with a novel taxonomy.\n\nThen we discuss the generalization problems in adversarial training from three perspectives and highlight the challenges which are not fully tackled.\n\nFinally, we present potential future directions.<\/jats:p>","DOI":"10.24963\/ijcai.2021\/591","type":"proceedings-article","created":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T11:00:49Z","timestamp":1628679649000},"page":"4312-4321","source":"Crossref","is-referenced-by-count":375,"title":["Recent Advances in Adversarial Training for Adversarial Robustness"],"prefix":"10.24963","author":[{"given":"Tao","family":"Bai","sequence":"first","affiliation":[{"name":"Nanyang Technological University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinqi","family":"Luo","sequence":"additional","affiliation":[{"name":"Nanyang Technological University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Zhao","sequence":"additional","affiliation":[{"name":"Nanyang Technological University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bihan","family":"Wen","sequence":"additional","affiliation":[{"name":"Nanyang Technological University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Wang","sequence":"additional","affiliation":[{"name":"Wuhan University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10584","event":{"name":"Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}","theme":"Artificial Intelligence","location":"Montreal, Canada","acronym":"IJCAI-2021","number":"30","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2021,8,19]]},"end":{"date-parts":[[2021,8,27]]}},"container-title":["Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T11:04:12Z","timestamp":1628679852000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2021\/591"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2021,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2021\/591","relation":{},"subject":[],"published":{"date-parts":[[2021,8]]}}}