Reviews: Dual Variational Generation for Low Shot Heterogeneous Face Recognition

UPDATE: After reading other reviewers' comments and the rebuttal, I decided to raise my score by one point from 6 to 7. I am satisfied with the effort the authors made to address my two major concerns and I recommend to accept this submission in agreement with the other reviewers. Overview/Contribution: The paper proposes a dual variational autoencoder to generate synthetic training data to combat the limited data in heterogeneous face recognition. The synthetic data tries to preserve identity via identity preserving generation both in the image and embedding spaces while providing sufficient variation for the training data of the downstream recognition task. Strengths: - Most facial recognition tasks involve certain assumptions that constrain the task into homogeneous set of inputs. Heterogeneous face recognition (HFR) is an important task for many practical applications that is attracting attention recently.