text: In this paper, the authors have proposed a GAN based method to conduct data augmentation.  The cross-class transformations are mapped to a low dimensional latent space using conditional GAN.  The paper is technically sound and the novelty is significant.  The motivation of the proposed methods is clearly illustrated.  Experiments on three datasets demonstrate the advantage of the proposed framework.  However, this paper still suffers from some drawbacks as below: (1)	The illustration of the framework is not clear enough.  For example, in figure 3, it says the GAN is designed for “class c”, which is ambiguous whether the authors trained only one network for all class or trained multiple networks and each is trained on one class. (2)	Some details is not clearly given, such as the dimension of the Gaussian distribution, the dimension of the projected  noise and . (3)	The proposed method needs to sample image pairs in each class.  As far as I am concerned, in most cases sampling strategy will affect the performance to some extent.  The authors need to show the robustness to sampling strategy of the proposed method.