Reviews: BinGAN: Learning Compact Binary Descriptors with a Regularized GAN

Summary This paper proposes a variant of GAN to learn compact binary descriptors for image patch matching. The authors introduce two novel regularizers to propagate Hamming distance between two layers in the discriminator and encourage the diversity of learned descriptors. The presentation is easy to follow, and the method is validated by benchmark datasets. Major concerns: [Motivation and Presentation] First of all, it's not so clear the reason why adversarial training helps to learn compact binary descriptors. In addition, the motivation on DMR is also not fully addressed in my sense. In my understanding, the discriminator has two binary representation layers; one of them has the larger number of bits and the other is used for the compact binary descriptor.