Domain adaptation tackles the problem of transferring knowledge from a labelrich source domain to an unlabeled or label-scarce target domain. Recently domain-adversarial training (DAT) has shown promising capacity to learn a domaininvariant 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.
###Summary###
This paper proposes Max-margin domain adversarial training (MDAT) to tackle the problem of transferring knowledge from a rich-labeled source domain to an unlabeled target domain. This is achieved by designing an adversarial reconstruction network. The proposed MDAT stabilizes the gradient by replacing the domain classifier with a reconstruction network. 
The motivation of the proposed network is based on the observations that the traditional domain-adversarial training is vulnerable in the following aspects:1) the training procedure of the domain discriminator is unstable, 2) it only considers the feature-level alignment, 3) it lacks the interpretable explanation for the learned feature space. 
In the proposed method, the Adversarial Reconstruction Network (ARN) consists of a shared feature extractor, a label predictor, and a reconstruction network. The reconstruction network only focuses on reconstructing samples on the source domain and pushing the target domain away from a margin. The feature extractor tries to confuse the decoder by learning to reconstruct samples on the target domain. 
The paper performs experiments on several domain adaptation tasks on digit datasets. The experimental results demonstrate the effectiveness of the proposed results over several baselines such as DANN, ADDA, CyCADA, CADA, etc. 
The paper also provides empirical analyses such as t-SNE embedding, plotting the loss, etc. to illustrate the effectiveness of the proposed approach. 
### Novelty ###
The model proposed in this paper is extended from the domain adversarial training approach. To stabilize the gradient, the model replaces the domain classifier with a reconstruction network. In this way, the discriminator only discriminates the reconstructed data from the source domain. This idea is interesting and provides some novelty.  
###Clarity###
Overall, the paper is well organized and logically clear. The claims are well-supported by the experiments. The images are well-presented and well-explained by the captions and the text. 
###Pros###
1) The paper proposes a Max-margin based approach to tackle domain adaptation. Instead of leveraging the domain discriminator to discriminate the source from the target, this paper utilizes a reconstructor to push the target domain far away from the margin. The idea is interesting and heuristic to the domain adaptation research community. 
2) The experimental results on digit benchmark demonstrate the effectiveness of the proposed method over other baselines including the most state-of-the-art ones. 
3) The paper provides many analyses to demonstrate the effectiveness of the proposed method. 
###Cons###
1) The experimental part of this paper is weak. The paper only provides experimental results on the digit recognition experiments, which is not enough to demonstrate the effectiveness and robustness of the proposed approach. Further experimental results on image recognition or NLP task is desired. 
It will be also interesting to see how does the proposed method perform on large-scale datasets such as DomainNet and Office-Home dataset:
DomainNet: Moment Matching for Multi-Source Domain Adaptation, ICCV 2019. http://ai.bu.edu/DomainNet/
Office-Home: Deep Hashing Network for Unsupervised Domain Adaptation, CVPR 2017. http://hemanthdv.org/OfficeHome-Dataset/
2) The organization and presentation of this paper should be polished.
Based on the summary, cons, and pros, the current rating I am giving now is weak reject. I would like to discuss the final rating with other reviewers, ACs.