Alargenumber ofveryinteresting anduseful applications have emerged due to the ability of GANs to learn complex real-world distributions.

Generative Adversarial Networks (GANs) have become a very popular tool for implicitly learning high-dimensional probability distributions.

I have read it carefully. The new experiments look good, but the authors do not seem to respond to my concern over SSIM metric between unpaired images. I keep my original review and rating. Given all the prior works that smooth GAN training, the idea that integrates image quality assessment metrics with GANs sounds interesting. From the experiment samples, it seems that the quality aware gan does improve the sample quality, the generated CelebA and STL images look sharp.