On Unifying Deep Generative Models: Supplementary Materials
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Supplementary Material for Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks
In the main text we derived Adversarial Variational Bayes (AVB) and demonstrated its usefulness both for black-box Variational Inference and for learning latent variable models. This document contains proofs that were omitted in the main text as well as some further details about the experiments and additional results.
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Deep generative models have achieved impressive success in recent years. Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), as powerful frameworks for deep generative model learning, have largely been considered as two distinct paradigms and received extensive independent studies respectively. This paper aims to establish formal connections between GANs and VAEs through...
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تاریخ انتشار 2017