Passing Tests without Memorizing: Two Models for Fooling Discriminators

arXiv: Learning, 2019.

Cited by: 4|Views26
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Abstract:

introduce two mathematical frameworks for foolability in the context of generative distribution learning. In a nuthsell, fooling is an algorithmic task in which the input sample is drawn from some target distribution and the goal is to output a synthetic distribution that is indistinguishable from the target w.r.t to some fixed class of ...More

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