Measure The Feature Universe: Topology-based Pseudo Labeling and Gravity Consistency for Source-Free Domain Adaptation

Source-free domain adaptation (SFDA) adapts a pretrained source model to an unlabeled target domain using only the model itself, typically relying on pseudo labeling augmented with auxiliary knowledge and consistency regularization (CR) mechanisms to alleviate noise in the generated pseudo labels. However, existing approaches overlook the geometric structure of the target embedding manifold when assigning pseudo labels, resulting in unreliable distance measurements and consequently severe mislabeling. Moreover, existing CR is applied solely to output logits, making it insensitive to feature-level reliability. To solve these issues, we propose a novel pseudo labeling scheme based on Feature universe, which is an expanded embedding space that models class-wise target distributions and Gravity consistency (GV) regularization, which modulates consistency strength according to feature-level similarity. Our pseudo labeling strategy first models the embedding space with virtual features to construct a feature universe. On this space, pseudo labels are generated through feature traversal, which propagates labels only from statistically reliable regions. In addition, GV jointly encourages logitand feature-level consistency, aligning predictions for augmented images while preserving the geometric structure of the embedding space. It further modulates the strength of CR for each sample, preventing the confirmation of noisy pseudo labels through a gravity-based force defined between two input embeddings. Experiments on Office-Home, DomainNet-126, and VisDA-C demonstrate consistent improvements over prior SFDA methods, and incorporating GV into baselines yields additional gains.