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- W3124304298 abstract "We propose a simple but effective modification of the discriminators, namely measure-conditional discriminators, as a plug-and-play module for different GANs. By taking the generated distributions as part of input so that the target optimum for the discriminator is stationary, the proposed discriminator is more robust than the vanilla one. A variant of the measure-conditional discriminator can also handle multiple target distributions, or act as a surrogate model of statistical distances such as KL divergence with applications to transfer learning." @default.
- W3124304298 created "2021-02-01" @default.
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- W3124304298 date "2021-01-17" @default.
- W3124304298 modified "2023-09-23" @default.
- W3124304298 title "Measure-conditional Discriminator with Stationary Optimum for GANs and Statistical Distance Surrogates." @default.
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