Unified Source-Free Domain Adaptation
Existing source-free domain adaptation (SFDA) methods are constrained to specific settings—e.g., closed-set, open-set, biased-set, or generalized SFDA—and rely on target-domain priors, limiting their applicability and theoretical grounding. Method: This work introduces Unified SFDA, the first formal problem formulation of SFDA that requires neither source data nor target-domain prior knowledge. From a causal perspective, it models the generative relationship between latent variables and decisions, proposing the Latent Causal Factor Discovery (LCFD) framework. LCFD integrates vision-language pretrained models (e.g., CLIP) with a causally motivated information bottleneck objective to achieve theoretically guaranteed representation disentanglement. Contribution/Results: Unified SFDA establishes a general, prior-free SFDA paradigm. It achieves state-of-the-art performance across all major SFDA benchmarks and significantly improves out-of-distribution generalization, demonstrating robustness to unseen domain shifts without access to source data or target annotations.