Venue: ICLR 2018 Workshop

Reviews:

final_score: rating: 6: Marginally above acceptance threshold
review_content: This paper proposes an end-to-end clustering method.
The problem of achieving the task of clustering by neural networks is an important topic to the conference, and the proposal is interesting and well described.
However, I have a concern regarding with the experimental setting.
The proposed method uses the information of whether or not a pair of clusters belong to the same cluster, which corresponds to the must-link and cannot-link in the context of semi-supervised clustering.
Hence, to evaluate the empirical performance of the proposed method, it should be compared with semi-supervised clustering methods for fair comparison.

review_title: Interesting paper but baselines of empirical comparison is not appropriate

final_score: rating: 1: Trivial or wrong
review_content: It requires y_ij, the supervised information that two items belong to a cluster or not. This is not cluster analysis.
review_title: This is a supervised method, not clustering.

final_score: rating: 5: Marginally below acceptance threshold
review_content: This works proposed a semi-supervised clustering algorithm consisting of 1) embedding network; 2) clustering network; 3) cluster-assignment network; and 4) cluster-count estimating network. The embedding network can use different data modalities as input, such as vectorial data, images and speeches. The supervision is in the form of pairwise constraints indicating whether a sample pair should belong to the same cluster. Experimental results on 2d synthetic data, image and speech data demonstrated the usefulness of the proposed approach.

First, the proposed approach belonged to the category of semi-supervised clustering. Therefore, for fair comparisons, instead of unsupervised learning approaches, the baselines should also be able to make use of pairwise constraints, such as constrained clustering (Wagstaff 2001) and metric learning (Xing 2002).

Second, could the authors provide more details to motivate the use of RBDLSTM for building the clustering network? Did the authors try vanilla feed-forward neural network or CNN as well?

Third, the performance of semi-supervised clustering algorithms heavily depended on the number of pairwise constraints. It seemed the authors used n(n-1)/2 constraints, which indicated all labels of the training samples were known. In real-world, getting the labels (or even pairwise constraints) can be very costly. It would be interesting to demonstrate how the proposed approach performed when the number of pairwise constraints varied from some small number to n(n-1)/2.
review_title: Well motivated, more experiments and baselines can improve the paper

Meta-review: Based on the reviews, this paper has not been accepted for presentation at the ICLR workshop. However, the conversation and updates can continue to appear here on OpenReview.
Decision: Reject