TARGET-DISCRIMINABILITY-INDUCED MULTI-SOURCE-FREE DOMAIN ADAPTATION

Source-free domain adaptation (SFDA) aims at target adaptation without access to source data, but with only pre-trained source model. Some recent works proposed to automatically combine source models with learnable weights when there are multiple pre-trained source models. In this paper, we propose a simple yet effective framework for multi-source-free domain adaptation (MSFDA). Specifically, based on discriminability towards target samples, we determine transferability of source models before adaptation and generate pseudo-labels during training. To quantify target discriminability, we introduce net confidence which refers to the probability difference between the largest and the second largest probabilities. We empirically show, on several benchmark datasets, our proposed method is competitive to the state-of-the-art methods.