Passing Tests without Memorizing: Two Models for Fooling Discriminators
arXiv: Learning, 2019.
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
PPT (Upload PPT)