Evidential Graph Contrastive Alignment for Source-Free Blending-Target Domain Adaptation

In this article, we first tackle a more realistic domain adaptation (DA) setting: source-free blending-target DA (SF-BTDA), where we cannot access to source-domain data while facing mixed multiple target domains without any domain labels in prior. Compared to existing DA scenarios, SF-BTDA generally faces the coexistence of different label shifts in different targets, along with noisy target pseudolabels generated from the source model. In this article, we propose a new method called evidential graph contrastive alignment (EGCA) to decouple the blending-target domain and alleviate the effect of noisy target pseudolabels. First, to improve the quality of pseudo target labels, we propose a calibrated evidential learning (CEL) module to iteratively improve both the accuracy and certainty of the resulting model and adaptively generate high-quality pseudo target labels. Second, we design a graph contrastive learning with the domain distance matrix and confidence-uncertainty criterion, to minimize the distribution gap of samples of the same class in the blending-target domain, which alleviates the coexistence of different label shifts in blended targets. We conduct a new benchmark based on three standard DA datasets, and EGCA outperforms other methods with considerable gains and achieves comparable results compared with those that have domain labels or source data in prior.