Deep neural networks suffer performance drops when source and target domains differ in distribution, motivating research into domain adaptation (DA). Traditional DA approaches presume source samples come from a single domain and can be available during adaptation. Nevertheless, in real-world applications, multiple source domains often exist, and source samples may be inaccessible owing to privacy and storage limitations. In response to the challenges of multi-source and source-free, multi-source-free domain adaptation (MSFDA) is proposed, which captures transferable information from a set of pre-trained source models to boost performance of the model on target domain. Most MSFDA methods meet these challenges by utilizing pseudo-labeling. However, pseudo-labels generated by distinct source models may contain noise and even be contradictory, which weakens their efficacy in facilitating source models adapting to the target domain. Moreover, these methods do not consider class imbalance, which would lead to biased predictions for minority classes, and undermine adaptation. Therefore, we propose a novel MSFDA method which extends adversarial learning to a multi-source-free setting. This method presents proxy multi-source domain adversarial learning, which aligns target features extracted by different source models in an adversarial manner, enhancing the capability of source models to extract domain-invariant features and potentially obtain high-quality pseudo-labels. Moreover, a nuclear-norm maximization regularization is employed to constrain prediction matrices, which can reduce the prediction uncertainty and enhance the discriminability of the model, while mitigating the prediction bias and promoting the prediction accuracy for minority classes. Finally, comprehensive evaluations on four benchmark datasets prove the validity of the proposed method.
Yang et al. (Fri,) studied this question.