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A Large-Scale Study on Regularization and Normalization in GANs
Karol Kurach · Mario Lucic · Xiaohua Zhai · Marcin Michalski · Sylvain Gelly

Wed Jun 12 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #9

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 number 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, as well as neural architectures. In this work we take a sober view of the current state of GANs from a practical perspective. We discuss and evaluate common pitfalls and reproducibility issues, open-source our code on Github, and provide pre-trained models on TensorFlow Hub.

Author Information

Karol Kurach (Google Brain)
Mario Lucic (Google Brain)
Xiaohua Zhai (Google Brain)
Marcin Michalski (Google Brain)
Sylvain Gelly (Google Brain)

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