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Fairness in Machine Learning
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강연 소개
강연 소개
Deep learning achieves remarkable success in various fields but still suffers from critical fairness issues in datasets and trained models. These challenges often incur unexpected results, which makes trained models unreliable, and it is extremely important to handle the problems effectively. This lecture first discusses various issues related to fairness, and reviews existing debiasing techniques to overcome such limitations. In addition to classification tasks, prevalent in fairness research, I also discuss additional challenges and the corresponding solutions in semantic segmentation.
초청 강연자
초청 강연자
한보형
한보형
서울대학교, 전기정보공학부 교수
약력
약력
Bohyung Han is a Professor in the Department of Electrical and Computer Engineering at Seoul National University, Korea. Before the current position, he was an Associate Professor in the Department of Computer Science and Engineering at POSTECH and a visiting research scientist in Google AI and Snap Research, both in Venice, CA, USA. He received a Ph.D. degree from the Department of Computer Science at the University of Maryland, College Park, MD, USA, in 2005. He served or will be serving as an organizing and technical program committee member numerous times in major computer vision and machine learning conferences. He is also an Associate Editor in TPAMI and MVA, and an Area Editor in CVIU. He received Google AI Focused Research Award in 2018, and his research group won the Visual Object Tracking (VOT) Challenge in 2015 and 2016.
주관
주관
인공지능 신호처리 소사이어티
후원
후원
계명대학교 AI융합연구소
In case of questions or unclear points, please contact niceko@kmu.ac.kr
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