  While domain adaptation has been actively researched in recent years, most
theoretical results and algorithms focus on the single-source-single-target
adaptation setting. Naive application of such algorithms on multiple source
domain adaptation problem may lead to suboptimal solutions. As a step toward
bridging the gap, we propose a new generalization bound for domain adaptation
when there are multiple source domains with labeled instances and one target
domain with unlabeled instances. Compared with existing bounds, the new bound
does not require expert knowledge about the target distribution, nor the
optimal combination rule for multisource domains. Interestingly, our theory
also leads to an efficient learning strategy using adversarial neural networks:
we show how to interpret it as learning feature representations that are
invariant to the multiple domain shifts while still being discriminative for
the learning task. To this end, we propose two models, both of which we call
multisource domain adversarial networks (MDANs): the first model optimizes
directly our bound, while the second model is a smoothed approximation of the
first one, leading to a more data-efficient and task-adaptive model. The
optimization tasks of both models are minimax saddle point problems that can be
optimized by adversarial training. To demonstrate the effectiveness of MDANs,
we conduct extensive experiments showing superior adaptation performance on
three real-world datasets: sentiment analysis, digit classification, and
vehicle counting.
