  Domain adaptation tackles the problem of transferring knowledge from a
label-rich source domain to a label-scarce or even unlabeled target domain.
Recently domain-adversarial training (DAT) has shown promising capacity to
learn a domain-invariant feature space by reversing the gradient propagation of
a domain classifier. However, DAT is still vulnerable in several aspects
including (1) training instability due to the overwhelming discriminative
ability of the domain classifier in adversarial training, (2) restrictive
feature-level alignment, and (3) lack of interpretability or systematic
explanation of the learned feature space. In this paper, we propose a novel
Max-margin Domain-Adversarial Training (MDAT) by designing an Adversarial
Reconstruction Network (ARN). The proposed MDAT stabilizes the gradient
reversing in ARN by replacing the domain classifier with a reconstruction
network, and in this manner ARN conducts both feature-level and pixel-level
domain alignment without involving extra network structures. Furthermore, ARN
demonstrates strong robustness to a wide range of hyper-parameters settings,
greatly alleviating the task of model selection. Extensive empirical results
validate that our approach outperforms other state-of-the-art domain alignment
methods. Moreover, reconstructing adapted features reveals the domain-invariant
feature space which conforms with our intuition.
