The GAN Landscape: Losses, Architectures, Regularization, and Normalization

Karol Kurach, Mario Lucic, Xiaohua Zhai, Marcin Michalski, Sylvain Gelly

Abstract: Generative adversarial networks (GANs) are a class of deep generative models which aim to learn a target distribution in an unsupervised fashion. While they were successfully applied to many problems, training a GAN is a notoriously challenging task and requires a significant amount of hyperparameter tuning, neural architecture engineering, and a non-trivial amount of ``tricks". The success in many practical applications coupled with the lack of a measure to quantify the failure modes of GANs resulted in a plethora of proposed losses, regularization and normalization schemes, and neural architectures. In this work we take a sober view of the current state of GANs from a practical perspective. We reproduce the current state of the art and go beyond fairly exploring the GAN landscape. We discuss common pitfalls and reproducibility issues, open-source our code on Github, and provide pre-trained models on TensorFlow Hub.

Keywords: GANs, empirical evaluation, large-scale, reproducibility

TL;DR: A sober view on the current state of GANs from a practical perspective

Data: [CelebA-HQ](https://paperswithcode.com/dataset/celeba-hq), [LSUN](https://paperswithcode.com/dataset/lsun)

Community Implementations: [ 9 code implementations](https://www.catalyzex.com/paper/the-gan-landscape-losses-architectures/code)