Geometric Anchor Correspondence Mining with Uncertainty Modeling for Universal Domain Adaptation

Universal domain adaptation (UniDA) aims to transfer the knowledge learned from a label-rich source domain to a label-scarce target domain without any constraints on the label space. However, domain shift and category shift make UniDA extremely challenging, which mainly lies in how to recognize both...

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Bibliographic Details

Published in:	Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) pp. 16113 - 16122
Main Authors:	Chen, Liang, Lou, Yihang, He, Jianzhong, Bai, Tao, Deng, Minghua
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.06.2022
Subjects:	Adaptation models Computational modeling Computer vision Logic gates Manifolds Representation learning Representation learning; Machine learning; Self-& semi-& meta- Transfer/low-shot/long-tail learning Uncertainty
ISSN:	1063-6919
Online Access:	Get full text
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