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Adversarial symmetric variational autoencoder

Publication ,  Journal Article
Pu, Y; Wang, W; Henao, R; Chen, L; Gan, Z; Li, C; Carin, L
Published in: Advances in Neural Information Processing Systems
January 1, 2017

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 benchmark datasets.

Duke Scholars

Published In

Advances in Neural Information Processing Systems

ISSN

1049-5258

Publication Date

January 1, 2017

Volume

2017-December

Start / End Page

4331 / 4340

Related Subject Headings

  • 4611 Machine learning
  • 1702 Cognitive Sciences
  • 1701 Psychology
 

Citation

APA
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ICMJE
MLA
NLM
Pu, Y., Wang, W., Henao, R., Chen, L., Gan, Z., Li, C., & Carin, L. (2017). Adversarial symmetric variational autoencoder. Advances in Neural Information Processing Systems, 2017-December, 4331–4340.
Pu, Y., W. Wang, R. Henao, L. Chen, Z. Gan, C. Li, and L. Carin. “Adversarial symmetric variational autoencoder.” Advances in Neural Information Processing Systems 2017-December (January 1, 2017): 4331–40.
Pu Y, Wang W, Henao R, Chen L, Gan Z, Li C, et al. Adversarial symmetric variational autoencoder. Advances in Neural Information Processing Systems. 2017 Jan 1;2017-December:4331–40.
Pu, Y., et al. “Adversarial symmetric variational autoencoder.” Advances in Neural Information Processing Systems, vol. 2017-December, Jan. 2017, pp. 4331–40.
Pu Y, Wang W, Henao R, Chen L, Gan Z, Li C, Carin L. Adversarial symmetric variational autoencoder. Advances in Neural Information Processing Systems. 2017 Jan 1;2017-December:4331–4340.

Published In

Advances in Neural Information Processing Systems

ISSN

1049-5258

Publication Date

January 1, 2017

Volume

2017-December

Start / End Page

4331 / 4340

Related Subject Headings

  • 4611 Machine learning
  • 1702 Cognitive Sciences
  • 1701 Psychology