Robust self-supervised learning for source-free domain adaptation

• Published in: Signal, image and video processing Vol. 17; no. 5; pp. 2405 - 2413
• Main Authors: Tian, Liang, Zhou, Lihua, Zhang, Hao, Wang, Zhenbin, Ye, Mao
• Format: Journal Article
• Language: English
• Published: London Springer London 01.07.2023; Springer Nature B.V
• Subjects: Adaptation; Computer Imaging; Computer Science; Domains; Image Processing and Computer Vision; Labels; Masks; Multimedia Information Systems; Original Paper; Pattern Recognition and Graphics; Robustness; Self-supervised learning; Signal,Image and Speech Processing; Vision; Local structure clustering; Source-free domain adaptation; Self-supervised learning

Source-free domain adaptation (SFDA) is from unsupervised domain adaptation (UDA) and do apply to the special situation in reality that the source domain data is not accessible. In this subject, self-supervised learning is widely used in previous works. However, inaccurate pseudo-labels are hardly a...