Source-Free Domain Adaptation via Target Prediction Distribution Searching

• Published in: International journal of computer vision Vol. 132; no. 3; pp. 654 - 672
• Main Authors: Tang, Song, Chang, An, Zhang, Fabian, Zhu, Xiatian, Ye, Mao, Zhang, Changshui
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2024; Springer Nature B.V
• Subjects: Adaptation; Algorithms; Alignment; Artificial Intelligence; Computer Imaging; Computer Science; Errors; Geodesic lines; Image Processing and Computer Vision; Paradigms; Pattern Recognition; Pattern Recognition and Graphics; Regularization; Searching; Vision; Domain adaptation; Pairwise alignment; Distribution searching; Geodesic path; Wasserstein distance
• ISSN: 0920-5691; 1573-1405
• DOI: 10.1007/s11263-023-01892-w

Existing Source-Free Domain Adaptation (SFDA) methods typically adopt the feature distribution alignment paradigm via mining auxiliary information (eg., pseudo-labelling, source domain data generation). However, they are largely limited due to that the auxiliary information is usually error-prone whilst lacking effective error-mitigation mechanisms. To overcome this fundamental limitation, in this paper we propose a novel Target Prediction Distribution Searching (TPDS) paradigm. Theoretically, we prove that in case of sufficient small distribution shift, the domain transfer error could be well bounded. To satisfy this condition, we introduce a flow of proxy distributions that facilitates the bridging of typically large distribution shift from the source domain to the target domain. This results in a progressive searching on the geodesic path where adjacent proxy distributions are regularized to have small shift so that the overall errors can be minimized. To account for the sequential correlation between proxy distributions, we develop a new pairwise alignment with category consistency algorithm for minimizing the adaptation errors. Specifically, a manifold geometry guided cross-distribution neighbour search is designed to detect the data pairs supporting the Wasserstein distance based shift measurement. Mutual information maximization is then adopted over these pairs for shift regularization. Extensive experiments on five challenging SFDA benchmarks show that our TPDS achieves new state-of-the-art performance. The code and datasets are available at https://github.com/tntek/TPDS .