Exploring category-agnostic clusters for open-set domain adaptation

• Main Authors: PAN, Yingwei, YAO, Ting, LI, Yehao, NGO, Chong-wah, MEI, Tao
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Computer Sciences; Graphics and Human Computer Interfaces
• Online Access: https://ink.library.smu.edu.sg/sis_research/6485; https://ink.library.smu.edu.sg/context/sis_research/article/7488/viewcontent/Pan_Exploring_Category_Agnostic_Clusters_for_Open_Set_Domain_Adaptation_CVPR_2020_paper.pdf
• Institution: Singapore Management University

Unsupervised domain adaptation has received significant attention in recent years. Most of existing works tackle the closed-set scenario, assuming that the source and target domains share the exactly same categories. In practice, nevertheless, a target domain often contains samples of classes unseen...