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Rethinking Generative Coverage: A Pointwise Guaranteed Approac

by   Peilin Zhong, et al.

All generative models have to combat missing modes. The conventional wisdom is by reducing a statistical distance (such as f-divergence) between the generated distribution and the provided data distribution through training. We defy this wisdom. We show that even a small statistical distance does not imply a plausible mode coverage, because this distance measures a global similarity between two distributions, but not their similarity in local regions--which is needed to ensure a complete mode coverage. From a starkly different perspective, we view the battle against missing modes as a two-player game, between a player choosing a data point and an adversary choosing a generator aiming to cover that data point. Enlightened by von Neumann's minimax theorem, we see that if a generative model can approximate a data distribution moderately well under a global statistical distance measure, then we should be able to find a mixture of generators which collectively covers every data point and thus every mode with a lower-bounded probability density. A constructive realization of this minimax duality--that is, our proposed algorithm of finding the mixture of generators--is connected to a multiplicative weights update rule. We prove the pointwise coverage guarantee of our algorithm, and our experiments on real and synthetic data confirm better mode coverage over recent approaches that also use a mixture of generators but focus on global statistical distances.


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