  As a study on the efficient usage of data, Multi-source Unsupervised Domain
Adaptation transfers knowledge from multiple source domains with labeled data
to an unlabeled target domain. However, the distribution discrepancy between
different domains and the noisy pseudo-labels in the target domain both lead to
performance bottlenecks of the Multi-source Unsupervised Domain Adaptation
methods. In light of this, we propose an approach that integrates
Attention-driven Domain fusion and Noise-Tolerant learning (ADNT) to address
the two issues mentioned above. Firstly, we establish a contrary attention
structure to perform message passing between features and to induce domain
movement. Through this approach, the discriminability of the features can also
be significantly improved while the domain discrepancy is reduced. Secondly,
based on the characteristics of the unsupervised domain adaptation training, we
design an Adaptive Reverse Cross Entropy loss, which can directly impose
constraints on the generation of pseudo-labels. Finally, combining these two
approaches, experimental results on several benchmarks further validate the
effectiveness of our proposed ADNT and demonstrate superior performance over
the state-of-the-art methods.
