How can we transfer knowledge from a source domain to a target domain when each side cannot observe the data in the other side? The recent state-of-theart deep architectures show significant performance in classification tasks which highly depend on a large number of training data. In order to resolve the dearth of abundant target labeled data, transfer learning and unsupervised learning leverage data from different sources and unlabeled data as training data, respectively. However, in some practical settings, transferring source data to target domain is restricted due to a privacy policy. In this paper, we define the problem of unsupervised domain adaptation under double blind constraint, where either the source or the target domain cannot observe the data in the other domain, but data from both domains are used for training. We propose TAN (Transfer Alignment Network for Double Blind Domain Adaptation), an effective method for the problem by aligning source and target domain features. TAN maps the target feature into source feature space so that the classifier learned from the labeled data in the source domain is readily used in the target domain. Extensive experiments show that TAN 1) provides the state-ofthe-art accuracy for double blind domain adaptation, and 2) outperforms baselines regardless of the proportion of target domain data in the training data.
###Summary###
This paper tackles the transfer learning problem with the double-blind unsupervised domain adaptation, where either the source or the target domain cannot observe the data in the other domain, but data from both domains are used for training. The high-level intuition of this paper is based on the observation that in some practical settings, the transferring source data to the target domain is restricted due to the privacy policy. The goal is to learn a classifier which performs well in target classification task under double-blind constraint. 
The setting of this paper is slightly different from the conventional domain adaptation. In this paper, the source domain has abundant unlabeled data and a small number of labeled data. The target domain only contains a limited number of unlabeled data.
The paper proposes a transfer alignment network (TAN) which comprises two autoencoders, one trained on the source domain and one trained on the target domain. In the domain adaptation phase, the model leverages an aligner to transfer the output of the target encoder to an aligned latent variable.  The aligner is trained to map the target code to source code on the target unlabeled data. The objective function is L2 distance between the source code and the mapped target code. 
The whole pipeline is trained with four steps:
1) The source encoder and source decoder are trained with L2 reconstruction loss.
2) The source encoder and source classifier are trained with cross-entropy classification loss.
3) The target encoder and target decoder are trained with L2 reconstruction loss.
4) Train the aligner to map target code to source code on target unlabeled data with L2 distance loss.
The paper proposes to compare the TAN with three baselines: S(UL):  a stack of encoder and a neural network classifier trained using source data and tested on target data without finetuning. S(UL)-T(U): a model retrains the S(UL) with the target unlabeled data. S(UL)-T(U)-Large: a model which is similar to S(UL)-T(U) but contains more layers and parameters in MLP.
The experiments are performed on five multivariate datasets: HIGGS, HEPMASS, SUSY, Sensorless, and Gas. 
### Novelty ###
The experimental setting proposed in this paper is interesting. However, the proposed model is trivial. The TAN model is composed of autoencoders and aligner. The training losses in the framework are L2 reconstruction loss and L2 distance loss. Thus, the novelty of this paper is incremental.
The experimental results in this paper are weak. First of all, the datasets used in this paper are not standard benchmarks. Secondly, the baselines in this paper are too trivial.   
###Clarity###
Overall, the paper is well organized and logically clear. The images are well-presented and well-explained by the captions and the text. 
###Pros###
1) The paper proposes an interesting transfer learning framework where either the source or the target domain cannot observe the data in the other domain. 
2) The paper is applicable to many practical scenarios since the data privacy in the real-world application is critical. 
3) The paper is overall well-organized. The claims of the paper are verified by the experimental results.
###Cons###
1) The paper proposes double-blind unsupervised domain adaptation as accessing the source and target domains is restricted in some practical settings. However, the source and target domain share the models trained on themselves, as well as the features extracted from the source domain and target domain data. The information about the original data can be recovered with the shared features and weights, which violates the settings proposed in this paper. 
2) The main issue of this paper is the novelty is incremental. The proposed model is trivial as it only contains the auto-encoders and L2 loss. 
3) The experimental part of this paper is weak. The datasets used in this paper are not the standard domain adaptation benchmark. It would be nice to see how does the proposed model work on standard domain adaptation benchmarks such as Office31, VisDA, Office-Home, DomainNet, etc.
VisDA: The Visual Domain Adaptation Challenge
https://arxiv.org/pdf/1710.06924.pdf
Office-Home : Deep Hashing Network for Unsupervised Domain Adaptation
http://hemanthdv.org/OfficeHome-Dataset/
The baselines used in this paper is also trivial. It is desirable to compare the proposed method with state-of-the-art domain adaptation methods.
Based on the summary, cons, and pros, the current rating I am giving now is "reject". I would like to discuss the final rating with other reviewers, ACs.