Learning Generative Models across Incomparable Spaces

14 May 2019Charlotte BunneDavid Alvarez-MelisAndreas KrauseStefanie Jegelka

Generative Adversarial Networks have shown remarkable success in learning a distribution that faithfully recovers a reference distribution in its entirety. However, in some cases, we may want to only learn some aspects (e.g., cluster or manifold structure), while modifying others (e.g., style, orientation or dimension)... (read more)

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