text: In this paper the authors give a nice review of clustering methods with deep learning and a systematic taxonomy for existing methods. Finally, the authors propose a new method by using one unexplored combination of taxonomy features. The paper is well-written and easy to follow. The proposed combination is straightforward, but lack of novelty. From table 1, it seems that the only differences between the proposed method and DEPICK is whether the method uses balanced assignment and pretraining. I am not convinced that these changes will lead to a significant difference. The performance of the proposed method and DEPICK are also similar in table 1. In addition, the experiments section is not comprehensive enough as well. the author only tested on two datasets. More datasets should be tested for evaluation. In addition, It seems that nearly all the experiments results from comparison methods are borrowed from the original publications. The authors should finish the experiments on comparison methods and fill the entries in Table 1. In summary, the proposed method is lack of novelty compare to existing methods. The survey part is nice, however extensive experiments should be conducted by running existing methods on different datasets and analyzing the pros and cons of the methods and their application scenarios. Therefore, I think the paper cannot be accepted at this stage.