Title: Confidence Score Weighting Adaptation for Source-Free Unsupervised Domain Adaptation. Abstract: Unsupervised domain adaptation (UDA) aims to achieve high performance within the unlabeled target domain by leveraging the labeled source domain.
Source-free UDA, which is a more challenging UDA task, can access the pre-trained model within the source domain.
The pre-trained model, however, provides a noisy pseudo-label; thus, source-free UDA requires robust training.
In this study, we propose a Confidence score Weighting Adaptation (CoWA), which is a simple yet effective source-free UDA method. 
CoWA utilizes the Joint Model-Data Structure (JMDS) confidence score designed for source-free UDA as a sample-wise weight.
As components of CoWA, we introduce Suppressed Cross Entropy (SCE) loss and a weight mixup to robustly leverage the low-confidence samples.
Experiment results show that CoWA achieves a superior performance compared to other source-free UDA methods on various UDA benchmarks including open-set and partial-set domain adaptation.
Furthermore, on several benchmarks, CoWA surpasses state-of-the-art conventional UDA methods that use labeled source domain data. 