Paper:
Deep Supervised Discrete Hashing
With the rapid growth of image and video data on the web, hashing has been extensively studied for image or video search in recent years. Benefiting from recent advances in deep learning, deep hashing methods have achieved promising results for image retrieval. However, there are some limitations of previous deep hashing methods (e.g., the semantic information is not fully exploited). In this paper, we develop a deep supervised discrete hashing algorithm based on the assumption that the learned binary codes should be ideal for classification. Both the pairwise label information and the classification information are used to learn the hash codes within one stream framework. We constrain the outputs of the last layer to be binary codes directly, which is rarely investigated in deep hashing algorithm. Because of the discrete nature of hash codes, an alternating minimization method is used to optimize the objective function. Experimental results have shown that our method outperforms current state-of-the-art methods on benchmark datasets.

Review:
The paper proposes a supervised hashing algorithm based on a neural network. This network aims to output the binary codes while optimizing the loss for the classification error. To jointly optimize two losses, the proposed method adopts an alternating strategy to minimize the objectives. Experiments on two datasets show good performance compared to other hashing approaches, including the ones use deep learning frameworks.
Pros:
The paper is written well and easy to follow. Generally, the idea is well-motivated, where the proposed algorithm and optimization strategy are sound and effective.
Cons:
Some references are missing as shown below, especially that [1] shares similar ideas. It is necessary to discuss [1] and carry out in-depth performance evaluation to clearly demonstrate the merits of this work. 
[1] Zhang et al., Efficient Training of Very Deep Neural Networks for Supervised Hashing, 
CVPR16
[2] Liu et al., Deep Supervised Hashing for Fast Image Retrieval, CVPR16
There are missing details for training the network (e.g., hyper-parameters), so it may not be able to reproduce the results accurately. It would be better if the authors will release the code if the paper is accepted.
Since the method adopts an alternating optimization approach, it would be better to show the curve of losses and demonstrate the effectiveness of the convergence.
The study in Figure 1 is interesting. However, the results and explanations (Ln 229-233) are not consistent. For example, DSDH-B is not always better than DSDH-A. In addition, the reason that DSDH-C is only slightly better than DSDH-A does not sound right (Ln 231-232). It may be the cause of different binarization strategies, and would require more experiments to validate it.
Why is DSDH-B better than DSDH in Figure 1(a) when using more bits?