Wasserstein Auto-Encoders: Difference between revisions
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= Introduction = | = Introduction = | ||
Recent years have seen a convergence of two previously distinct approaches: representation learning from high dimensional data, and unsupervised generative modeling. In | Recent years have seen a convergence of two previously distinct approaches: representation learning from high dimensional data, and unsupervised generative modeling. In the field that formed at the intersection, Variational Auto-Encoders (VAEs) and Generative Adversarial Networks (GANs) have emerged to be the most popular. | ||
= Motivation = | = Motivation = | ||
Revision as of 21:40, 11 March 2018
Introduction
Recent years have seen a convergence of two previously distinct approaches: representation learning from high dimensional data, and unsupervised generative modeling. In the field that formed at the intersection, Variational Auto-Encoders (VAEs) and Generative Adversarial Networks (GANs) have emerged to be the most popular.