🤖 AI Summary
This work proposes a source-free unsupervised domain adaptation method that operates without access to source domain data. By optimizing the intra-batch cosine similarity and dissimilarity of target-domain samples and incorporating a neighborhood signature–based clustering mechanism, the approach effectively suppresses interference from noisy neighbors and constructs more discriminative cluster structures. Notably, the method relies solely on a single loss term, substantially simplifying the adaptation process. Extensive experiments on benchmark datasets—including VisDA—demonstrate its state-of-the-art performance, with particularly significant gains on VisDA that underscore both its effectiveness and novelty.
📝 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.