Bridging the Gap Between $f$-GANs and Wasserstein GANs

22 Oct 2019Jiaming SongStefano Ermon

Generative adversarial networks (GANs) have enjoyed much success in learning high-dimensional distributions. Learning objectives approximately minimize an $f$-divergence ($f$-GANs) or an integral probability metric (Wasserstein GANs) between the model and the data distribution using a discriminator... (read more)

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