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"Best-of-Many-Samples" Distribution Matching


Bhattacharyya,  Apratim
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society;


Schiele,  Bernt
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society;

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Bhattacharyya, A., Fritz, M., & Schiele, B. (2019). "Best-of-Many-Samples" Distribution Matching. Retrieved from http://arxiv.org/abs/1909.12598.

Cite as: http://hdl.handle.net/21.11116/0000-0005-554A-9
Generative Adversarial Networks (GANs) can achieve state-of-the-art sample quality in generative modelling tasks but suffer from the mode collapse problem. Variational Autoencoders (VAE) on the other hand explicitly maximize a reconstruction-based data log-likelihood forcing it to cover all modes, but suffer from poorer sample quality. Recent works have proposed hybrid VAE-GAN frameworks which integrate a GAN-based synthetic likelihood to the VAE objective to address both the mode collapse and sample quality issues, with limited success. This is because the VAE objective forces a trade-off between the data log-likelihood and divergence to the latent prior. The synthetic likelihood ratio term also shows instability during training. We propose a novel objective with a "Best-of-Many-Samples" reconstruction cost and a stable direct estimate of the synthetic likelihood. This enables our hybrid VAE-GAN framework to achieve high data log-likelihood and low divergence to the latent prior at the same time and shows significant improvement over both hybrid VAE-GANS and plain GANs in mode coverage and quality.