  Domain adaptation tackles the problem of transferring knowledge from a label-rich source domain to an unlabeled or label-scarce 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. Additionally, the reconstructed target samples are visualized to interpret the domain-invariant feature space which conforms with our intuition. Deep neural networks have gained great success on a wide range of tasks such as visual recognition and machine translation (LeCun et al., 2015) . They usually require a large number of labeled data that can be prohibitively expensive to collect, and even with sufficient supervision their performance can still be poor when being generalized to a new environment. The problem of discrepancy between the training and testing data distribution is commonly referred to as domain shift (Shimodaira, 2000) . To alleviate the effect of such shift, domain adaptation sets out to obtain a model trained in a label-rich source domain to generalize well in an unlabeled target domain. Domain adaptation has benefited various applications in many practical scenarios, including but not limited to object detection under challenging conditions (Chen et al., 2018) , cost-effective learning using only synthetic data to generalize to real-world imagery (Vazquez et al., 2013) , etc. Prevailing methods for unsupervised domain adaptation (UDA) are mostly based on domain alignment which aims to learn domain-invariant features by reducing the distribution discrepancy between the source and target domain using some pre-defined metrics such as maximum mean discrepancy (Tzeng et al., 2014) . Recently, Ganin & Lempitsky (2015) proposed to achieve domain alignment by domainadversarial training (DAT) that reverses the gradients of a domain classifier to maximize domain confusion. Having yielded remarkable performance gain, DAT was employed in many subsequent UDA methods (Long et al., 2018; Shu et al., 2018) . Even so, there still exist three critical issues of DAT that hinder its performance: (1) as the domain classifier has high-capacity to discriminate two domains, the unbalanced adversarial training cannot continuously provide effective gradients, which is usually overcome by manually adjusting the weights of adversarial training according to specific tasks; (2) DAT-based methods cannot deal with pixel-level domain shift (Hoffman et al., 2018) ; (3) the domain-invariant features learned by DAT are only based on intuition but difficult to interpret, which impedes the investigation of the underlying mechanism of adversarial domain adaptation. To overcome the aforementioned difficulties, we propose an innovative DAT approach, namely Max-margin Domain-Adversarial Training (MDAT), to realize stable and comprehensive domain alignment. To demonstrate its effectiveness, we develop an Adversarial Reconstruction Network (ARN) that only utilizes MDAT for UDA. Specifically, ARN consists of a shared feature extractor, a label predictor, and a reconstruction network (i.e. decoder) that serves as a domain classifier. Supervised learning is conducted on source domain, and MDAT helps learn domain-invariant features. In MDAT, the decoder only focuses on reconstructing samples on source domain and pushing the target domain away from a margin, while the feature extractor aims to fool the decoder by learning to reconstruct samples on target domain. In this way, three critical issues can be solved by MDAT: (1) the max-margin loss reduces the discriminative capacity of domain classifier, leading to balanced and thus stable adversarial training; (2) without involving new network structures, MDAT achieves both pixel-level and feature-level domain alignment; (3) visualizing the reconstructed samples reveals how the source and target domains are aligned. We evaluate ARN with MDAT on five visual and non-visual UDA benchmarks. It achieves significant improvement to DAT on all tasks with pixel-level or higher-level domain shift. We also observe that it is insensitive to the choices of hyperparameters and as such is favorable for replication in practice. In principle, our approach is generic and can be used to enhance any UDA methods that leverage domain alignment as an ingredient. Compared with the conventional DAT-based methods that are usually based on a binary logistic network (Ganin & Lempitsky, 2015) , the proposed ARN with MDAT is more attractive and incorporates new merits conceptually and theoretically: (1) Stable training and insensitivity to hyper-parameters. Using the decoder as domain classifier with a margin loss to restrain its overwhelming capacity in adversarial training, the minimax game can continuously provide effective gradients for training the feature extractor. Moreover, through the experiments in Section 4, we discover that our method shows strong robustness to the hyperparameters, i.e. α and m, greatly alleviating the parameters tuning for model selection. (2) Richer information for comprehensive domain alignment. Rather than DAT that uses a bit of domain information, MDAT utilizes the reconstruction network as the domain classifier that could capture more domain-specific and pixel-level features during the unsupervised reconstruction (Bousmalis et al., 2016) . Therefore, MDAT further helps address pixel-level domain shift apart from the feature-level shift, leading to comprehensive domain alignment in a straightforward manner. (3) Feature visualization for method validation. Another key merit of MDAT is that MDAT allows us to visualize the features directly by the reconstruction network. It is crucial to understand to what extent the features are aligned since this helps to reveal the underlying mechanism of adversarial domain adaptation. We will detail the interpretability of these adapted features in Section 4.3. We proposed a new domain alignment approach namely max-margin domain-adversarial training (MDAT) and a MDAT-based network for unsupervised domain adaptation. The proposed method offers effective and stable gradients for the feature learning via an adversarial game between the feature extractor and the reconstruction network. The theoretical analysis provides justifications on how it minimizes the distribution discrepancy. Extensive experiments demonstrate the effectiveness of our method and we further interpret the features by visualization that conforms with our insight. Potential evaluation on semi-supervised learning constitutes our future work.
A stable domain-adversarial training approach for robust and comprehensive domain adaptation
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