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Stein Variational Autoencoder

Abstract · Apr 18, 2017 00:08 ·

kingma variational latent rezende encoder welling 2014 vae cs-lg

Arxiv Abstract

  • Yunchen Pu
  • Zhe Gan
  • Ricardo Henao
  • Chunyuan Li
  • Shaobo Han
  • Lawrence Carin

A new method for learning variational autoencoders is developed, based on an application of Stein’s operator. The framework represents the encoder as a deep nonlinear function through which samples from a simple distribution are fed. One need not make parametric assumptions about the form of the encoder distribution, and performance is further enhanced by integrating the proposed encoder with importance sampling. Example results are demonstrated across multiple unsupervised and semi-supervised problems, including semi-supervised analysis of the ImageNet data, demonstrating the scalability of the model to large datasets.

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