Source-Free Domain Adaptation Using Neighborhood Signature–Based Prediction Matching

TMLR Paper5305 Authors

Abstract: Source-Free Domain Adaptation (SFDA) is an emerging area of research that aims to adapt a model trained on a labeled source domain to an unlabeled target domain without accessing the source data. Most of the successful methods in this area rely on the concept of neighborhood consistency but are prone to errors due to misleading neighborhood information. In this paper, we explore this approach from the point of view of learning more informative clusters and mitigating the effect of noisy neighbors using a concept called neighborhood signature, and demonstrate that adaptation can be achieved using just a single loss term tailored to optimize the similarity and dissimilarity of predictions of samples in the target domain. In particular, our proposed method outperforms existing methods in the challenging VisDA dataset while also yielding competitive results on other benchmark datasets.

Submission Length: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: ~Fredrik_Daniel_Johansson1

Submission Number: 5305