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arXiv:1705.05263cs.LG

Comparison of Maximum Likelihood and GAN-based training of Real NVPs

Ivo Danihelka, Balaji Lakshminarayanan, Benigno Uria, Daan Wierstra, Peter Dayan

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Abstract

We train a generator by maximum likelihood and we also train the same generator architecture by Wasserstein GAN. We then compare the generated samples, exact log-probability densities and approximate Wasserstein distances. We show that an independent critic trained to approximate Wasserstein distance between the validation set and the generator distribution helps detect overfitting. Finally, we use ideas from the one-shot learning literature to develop a novel fast learning critic.

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cs.LGMachine Learning

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  1. Progressive Growing of GANs for Improved Quality, Stability, Variation [video] 1 comment

    • This is a general problem for generative models, regardless of whether they are explicit latent density (variational autoencoders) or implicit density (GANs) or fully-observed (PixelCNN). To date no consensus has been reached in the research community on what the "right" way to…

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