Timezone: »
In this paper, we analyze the numerics of common algorithms for training Generative Adversarial Networks (GANs). Using the formalism of smooth two-player games we analyze the associated gradient vector field of GAN training objectives. Our findings suggest that the convergence of current algorithms suffers due to two factors: i) presence of eigenvalues of the Jacobian of the gradient vector field with zero real-part, and ii) eigenvalues with big imaginary part. Using these findings, we design a new algorithm that overcomes some of these limitations and has better convergence properties. Experimentally, we demonstrate its superiority on training common GAN architectures and show convergence on GAN architectures that are known to be notoriously hard to train.
Author Information
Lars Mescheder (Max-Planck Institute Tuebingen)
Sebastian Nowozin (Microsoft Research Cambridge)
Andreas Geiger (MPI Tübingen)
Related Events (a corresponding poster, oral, or spotlight)
-
2017 Poster: The Numerics of GANs »
Thu Dec 7th 02:30 -- 06:30 AM Room Pacific Ballroom #101
More from the Same Authors
-
2018 Workshop: Smooth Games Optimization and Machine Learning »
Simon Lacoste-Julien · Ioannis Mitliagkas · Gauthier Gidel · Vasilis Syrgkanis · Eva Tardos · Leon Bottou · Sebastian Nowozin -
2017 Poster: Stabilizing Training of Generative Adversarial Networks through Regularization »
Kevin Roth · Aurelien Lucchi · Sebastian Nowozin · Thomas Hofmann