Hi everyone, I've recently built Mimicry, a PyTorch library for GANs which I hope can make GAN research findings more reproducible. The general idea is to have an easily accessible set of implementations (that reproduce the original scores as closely as possible), baseline scores for comparisons, and metrics for GANs which researchers can quickly use to produce results and compare. For reproducibility, I re-implemented the original models and verified their correctness by checking their scores against the reported ones under the same training and evaluation conditions. On the metrics part, to ensure backward compatibility of existing scores, I adopted the original TensorFlow implementations of Inception Score, FID, and KID so new scores produced can be compared with other works directly. I've also included a tutorial to implement a more sophisticated GAN like Self-supervised GAN (SSGAN) from the ground up, again with a focus on reproducing the results.

I would recommend starting your GAN journey with the DCGAN paper. This paper shows how convolutional layers can be used with GANs and provides a series of additional architectural guidelines for doing this. The paper also discusses topics such as Visualizing GAN features, Latent space interpolation, using discriminator features to train classifiers, and evaluating results. All of these additional topics are bound to come up in your GAN research. In summation, the DCGAN paper is a must-read GAN paper because it defines the architecture in such a clear way that it is easy to get started with some code and begin developing an intuition for GANs.