NeurIPS 2019
Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center
Paper ID:1693
Title:Quality Aware Generative Adversarial Networks

The paper proposes a novel way to regularize training of deep adversarial generative models for natural images. The proposal is based on using the image quality metrics. While many different ways of stabilizing and regularizing GAN training were proposed in prior work, most of which based on various gradient penalties related to the Lipschitzness, this submission proposes an idea which is significantly different and novel. The paper evaluates the new method on three reasonably challenging datasets (CIFAR-10, STL-10, CelebA) and quantitatively shows objective advantages to other methods (in terms of FID and IS). The field of GANs and in particular various ways to stabilize their training has been recently attracting perhaps excessive amount of attention with many papers proposing multiple methods very similar in nature. Seeing sufficiently new ideas (even if they feel incremental) being introduced to this field is refreshing. I would support acceptance of this paper.