text: This paper proposes two regularization terms to encourage learning disentangled representations.  One term is applied to weight parameters of a layer just like weight decay.  The other is applied to the activations of the target layer (e.g., the penultimate layer).  The core part of both regularization terms is a compound hinge loss of which the input is the KL divergence between two softmax-normalized input arguments.  Experiments demonstrate the proposed regularization terms are helpful in learning representations which significantly facilitate clustering performance. Pros: (1) This paper is clearly written and easy to follow. (2) Authors proposed multiple variants of the regularization term which cover both supervised and unsupervised settings. (3) Authors did a variety of classification experiments ranging from time serials, image and text data. Cons: (1) The design choice of the compound hinge loss is a bit arbitrary.  KL divergence is a natural similarity measure for probability distribution.  However, it seems that authors use softmax to force the weights or the activations of neural networks to be probability distributions just for the purpose of using KL divergence.  Have you compared with other choices of similarity measure, e.g., cosine similarity?  I think the comparison as an additional experiment would help explain the design choice of the proposed function. (2) In the binary classification experiments, it is very strange to almost randomly group several different classes of images into the same category.  I would suggest authors look into datasets where the class hierarchy is already provided, e.g., ImageNet or a combination of several fine-grained image classification datasets. Additionally, I have the following questions: (1) I am curious how the proposed method compares to other competitors in terms of the original classification setting, e.g., 10-class classification accuracy on CIFAR10.  (2) What will happen for the multi-layer loss if the network architecture is very large such that you can not use large batch size, e.g., less than 10?  (3) In drawing figure 2 and 3, if the nonlinear activation function is not ReLU, how would you exam the same behavior?  Have you tried multi-class classification for the case “without proposed loss component” and does the similar pattern still happen or not? Some typos: (1) In introduction, “when the cosine between the vectors 1” should be “when the cosine between the vectors is 1”. (2) In section 4.3, “we used the DBPedia ontology dataset dataset” should be “we used the DBPedia ontology dataset”.  I would like to hear authors’ feedback on the issues I raised.