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arXiv · 2006.04596

Learning disconnected manifolds: a no GANs land

Ugo Tanielian·Thibaut Issenhuth·Elvis Dohmatob·Jeremie Mary
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Abstract

Typical architectures of Generative AdversarialNetworks make use of a unimodal latent distribution transformed by a continuous generator. Consequently, the modeled distribution always has connected support which is cumbersome when learning a disconnected set of manifolds. We formalize this problem by establishing a no free lunch theorem for the disconnected manifold learning stating an upper bound on the precision of the targeted distribution. This is done by building on the necessary existence of a low-quality region where the generator continuously samples data between two disconnected modes. Finally, we derive a rejection sampling method based on the norm of generators Jacobian and show its efficiency on several generators including BigGAN.

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Ugo Tanielian, Thibaut Issenhuth, Elvis Dohmatob, Jeremie Mary. 2020-06-08. Learning disconnected manifolds: a no GANs land. https://arxiv.org/abs/2006.04596

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