Dual Classifier Adaptation: Source-Free UDA via Adaptive Pseudo-Labels Learning

Abstract Different from Unsupervised Domain Adaptation (UDA), Source-Free Unsupervised Domain Adaptation (SFUDA) transfers source knowledge to target domain without accessing the source data, using only the source model, has attracted much attention recently. One mainstream SFUDA method fine-tunes the source model by self-training to generate pseudo-labels of the target data. However, due to the significant differences between different domains, these target pseudo-labels often contain some noise, and it will inevitably degenerates the target performance. For this purpose, we propose an innovative SFUDA method with adaptive pseudo-labels learning named Dual Classifier Adaptation (DCA). In DCA, a dual classifier structure is introduced to adaptively learn target pseudo-labels by cooperation between source and target classifiers. Simultaneously, the minimax entropy is introduced for target learning, in order to adapt target data to source model, while capture the intrinsic cluster structure in target domain as well. After compared our proposed method DCA with a range of UDA and SFUDA methods, DCA achieves far ahead performance on several benchmark datasets.