{"type":"ScholarlyArticle","title":"Recent Advances in Adversarial Training for Adversarial Robustness","authors":[{"name":"Tao Bai","slug":"tao-bai","given":"Tao","family":"Bai","affiliation":""},{"name":"Jinqi Luo","slug":"jinqi-luo","given":"Jinqi","family":"Luo","affiliation":""},{"name":"Jun Zhao","slug":"jun-zhao","given":"Jun","family":"Zhao","affiliation":""},{"name":"Bihan Wen","slug":"bihan-wen","given":"Bihan","family":"Wen","affiliation":""},{"name":"Qian Wang","slug":"qian-wang","given":"Qian","family":"Wang","affiliation":""}],"abstract":"Adversarial training is one of the most effective approaches for deep learning models to defend against adversarial examples.\r\nUnlike other defense strategies, adversarial training aims to enhance the robustness of models intrinsically.\r\nDuring the past few years, adversarial training has been studied and discussed from various aspects, which deserves a comprehensive review.\r\nFor the first time in this survey, we systematically review the recent progress on adversarial training for adversarial robustness with a novel taxonomy.\r\nThen we discuss the generalization problems in adversarial training from three perspectives and highlight the challenges which are not fully tackled.\r\nFinally, we present potential future directions.","doi":"10.24963/ijcai.2021/591","url":"https://ijcai.org/proceedings/2021/591","pdf":"https://ijcai.org/proceedings/2021/0591.pdf","markdown":"https://ijcai.org/proceedings/2021/591.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/591","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Survey Track","track":"Survey Track","number":591,"pages":{"first":4312,"last":4321},"keywords":["Computer vision: General","Machine learning: General"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}