Rethinking Generative Mode Coverage: A Pointwise Guaranteed Approach
Peilin Zhong,
Yuchen Mo,
Chang Xiao
et al.
Abstract:Many generative models have to combat missing modes. The conventional wisdom to this end is by reducing through training a statistical distance (such as f -divergence) between the generated distribution and provided data distribution. But this is more of a heuristic than a guarantee. The statistical distance measures a global, but not local, similarity between two distributions. Even if it is small, it does not imply a plausible mode coverage. Rethinking this problem from a game-theoretic perspective, we show … Show more
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