Adversarial Symmetric Variational Autoencoder

A new form of variational autoencoder (VAE) is developed, in which the joint distribution of data and codes is considered in two (symmetric) forms: (i) from observed data fed through the encoder to yield codes, and (ii) from latent codes drawn from a simple prior and propagated through the decoder to manifest data. Lower bounds are learned for marginal log-likelihood fits observed data and latent codes. When learning with the variational bound, one seeks to minimize the symmetric Kullback-Leibler divergence of joint density functions from (i) and (ii), while simultaneously seeking to maximize the two marginal log-likelihoods. To facilitate learning, a new form of adversarial training is developed. An extensive set of experiments is performed, in which we demonstrate state-of-the-art data reconstruction and generation on several image benchmarks datasets.

Authors

• Yuchen Pu Duke University
• Weiyao Wang Duke University
• Ricardo Henao Duke University
• Liqun Chen Huazhong University of Science and Technology National Key Laboratory of Multispectral Information Intelligent Processing Technology
• Zhe Gan
• Chunyuan Li ByteDance Inc.
• Lawrence Carin

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