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• W2982620140 abstract "Generative models produce realistic objects in many domains, including text, image, video, and audio synthesis. Most popular models---Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs)---usually employ a standard Gaussian distribution as a prior. Previous works show that the richer family of prior distributions may help to avoid the mode collapse problem in GANs and to improve the evidence lower bound in VAEs. We propose a new family of prior distributions---Tensor Ring Induced Prior (TRIP)---that packs an exponential number of Gaussians into a high-dimensional lattice with a relatively small number of parameters. We show that these priors improve Frechet Inception Distance for GANs and Evidence Lower Bound for VAEs. We also study generative models with TRIP in the conditional generation setup with missing conditions. Altogether, we propose a novel plug-and-play framework for generative models that can be utilized in any GAN and VAE-like architectures." @default.
• W2982620140 created "2019-11-08" @default.
• W2982620140 creator A5021794857 @default.
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• W2982620140 date "2019-10-29" @default.
• W2982620140 modified "2023-09-27" @default.
• W2982620140 title "A Prior of a Googol Gaussians: a Tensor Ring Induced Prior for Generative Models." @default.
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• W2982620140 hasPublicationYear "2019" @default.
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