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Evaluating GANs via Duality

ICLR 2019 • Paulina Grnarova • Kfir. Y. Levy • Aurelien Lucchi • Nathanael Perraudin • Thomas Hofmann • Andreas Krause

Generative Adversarial Networks (GANs) have shown great results in accurately modeling complex distributions, but their training is known to be difficult due to instabilities caused by a challenging minimax optimization problem. This is especially troublesome given the lack of an evaluation metric that can reliably detect non-convergent behaviors... (read more)

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