Title: Joint Attention-Driven Domain Fusion and Noise-Tolerant Learning for Multi-Source Domain Adaptation. Abstract: Multi-source Unsupervised Domain Adaptation (MUDA) transfers knowledge from multiple source domains with labeled data to an unlabeled target domain.
Recently, endeavours have been made in establishing connections among different domains to enable feature interaction. However, these approaches essentially enhance category information and thus lack the transfer of the domain-specific information. Moreover, few research has explored the connection between pseudo-label generation and the framework’s learning capabilities, crucial for ensuring robust MUDA. In this paper, we propose a novel framework, which significantly reduces the domain discrepancy and demonstrates new state-of-the-art performance. In particular, we first propose a Contrary Attention-based Domain Merge (CADM) module to enable the interaction among the features so as to achieve the mixture of domain-specific information instead of focusing on the category information. Secondly, to enable the network to correct the pseudo labels during training, we propose an adaptive and reverse cross-entropy loss, which can adaptively impose constraints on the pseudo-label generation process. We conduct experiments on four benchmark datasets, showing that our approach can efficiently fuse all domains for MUDA while showing much better performance than the prior methods.