  Generative Adversarial Networks (GANs) have shown remarkable results in
modeling complex distributions, but their evaluation remains an unsettled
issue. Evaluations are essential for: (i) relative assessment of different
models and (ii) monitoring the progress of a single model throughout training.
The latter cannot be determined by simply inspecting the generator and
discriminator loss curves as they behave non-intuitively. We leverage the
notion of duality gap from game theory to propose a measure that addresses both
(i) and (ii) at a low computational cost. Extensive experiments show the
effectiveness of this measure to rank different GAN models and capture the
typical GAN failure scenarios, including mode collapse and non-convergent
behaviours. This evaluation metric also provides meaningful monitoring on the
progression of the loss during training. It highly correlates with FID on
natural image datasets, and with domain specific scores for text, sound and
cosmology data where FID is not directly suitable. In particular, our proposed
metric requires no labels or a pretrained classifier, making it domain
agnostic.
