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 the field that formed at the intersection, Variational Auto-Encoders (VAEs) and Generative Adversarial Networks (GANs) have emerged to be | 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 well-known. VAEs are theoretically elegant but with the drawback that they tend to generate blurry samples when applied to natural images. GANs on the other hand produce better visual quality of sampled images, but come without an encoder, are harder to train and suffer from the mode-collapse problem when the trained model is unable to capture all the variability in the true data distribution. | ||
= Motivation = | = Motivation = | ||
Revision as of 22:44, 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 well-known. VAEs are theoretically elegant but with the drawback that they tend to generate blurry samples when applied to natural images. GANs on the other hand produce better visual quality of sampled images, but come without an encoder, are harder to train and suffer from the mode-collapse problem when the trained model is unable to capture all the variability in the true data distribution.