  Deep neural networks suffer from performance decay when there is domain shift
between the labeled source domain and unlabeled target domain, which motivates
the research on domain adaptation (DA). Conventional DA methods usually assume
that the labeled data is sampled from a single source distribution. However, in
practice, labeled data may be collected from multiple sources, while naive
application of the single-source DA algorithms may lead to suboptimal
solutions. In this paper, we propose a novel multi-source distilling domain
adaptation (MDDA) network, which not only considers the different distances
among multiple sources and the target, but also investigates the different
similarities of the source samples to the target ones. Specifically, the
proposed MDDA includes four stages: (1) pre-train the source classifiers
separately using the training data from each source; (2) adversarially map the
target into the feature space of each source respectively by minimizing the
empirical Wasserstein distance between source and target; (3) select the source
training samples that are closer to the target to fine-tune the source
classifiers; and (4) classify each encoded target feature by corresponding
source classifier, and aggregate different predictions using respective domain
weight, which corresponds to the discrepancy between each source and target.
Extensive experiments are conducted on public DA benchmarks, and the results
demonstrate that the proposed MDDA significantly outperforms the
state-of-the-art approaches. Our source code is released at:
https://github.com/daoyuan98/MDDA.
