Energy-Based Pseudo-Label Refining for Source-free Domain Adaptation
This work improves domain adaptation for scenarios where source data is unavailable, though it appears incremental as it builds on existing SFDA techniques.
The paper tackled the problem of source-free domain adaptation by addressing noise in pseudo-labels, resulting in a model that outperformed state-of-the-art methods on datasets like Office-31, Office-Home, and VisDA-C.
Source-free domain adaptation (SFDA), which involves adapting models without access to source data, is both demanding and challenging. Existing SFDA techniques typically rely on pseudo-labels generated from confidence levels, leading to negative transfer due to significant noise. To tackle this problem, Energy-Based Pseudo-Label Refining (EBPR) is proposed for SFDA. Pseudo-labels are created for all sample clusters according to their energy scores. Global and class energy thresholds are computed to selectively filter pseudo-labels. Furthermore, a contrastive learning strategy is introduced to filter difficult samples, aligning them with their augmented versions to learn more discriminative features. Our method is validated on the Office-31, Office-Home, and VisDA-C datasets, consistently finding that our model outperformed state-of-the-art methods.