text: The paper studies the problem of DNN loss function design for reducing intra-class variance in the output feature space.  The key contribution is proposing an isotropic variant of the softmax loss that can balance the accuracy of classification and compactness of individual class.  The proposed loss has been compared extensively against a number of closely related approaches in methodology.  Numerical results on benchmark datasets show some improvement of the proposed loss over softmax loss and center loss (Wen et al., 2016), when applied to distance-based classifiers such as k-NN and k-means.  Pros: - The idea of isotropic normalization for enhancing compactness of class is well motivated - The paper is mostly clearly organized and presented. - Numerical study shows some promise of the proposed method. Cons: -  The novelty of method is mostly incremental given the prior work of (Wen et al., 2016) which has provided a slightly different isotropic variant of softmax loss. - The training procedure of the proposed method remains unclear in this paper.