Symmetric variational autoencoder and connections to adversarial learning

Liqun Chen; Shuyang Dai; Yunchen Pu; Erjin Zhou; Chunyuan Li; Qinliang Su; Changyou Chen; Lawrence Carin

Symmetric variational autoencoder and connections to adversarial learning

Liqun Chen, Shuyang Dai, Yunchen Pu, Erjin Zhou, Chunyuan Li, Qinliang Su, Changyou Chen, Lawrence Carin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

A new form of the variational autoencoder (VAE) is proposed, based on the symmetric Kullback-Leibler divergence. It is demonstrated that learning of the resulting symmetric VAE (sVAE) has close connections to previously developed adversarial-learning methods. This relationship helps unify the previously distinct techniques of VAE and adversarially learning, and provides insights that allow us to ameliorate shortcomings with some previously developed adversarial methods. In addition to an analysis that motivates and explains the sVAE, an extensive set of experiments validate the utility of the approach.

Original language	English (US)
Title of host publication	International Conference on Artificial Intelligence and Statistics, AISTATS 2018
Publisher	PMLR
Pages	661-669
Number of pages	9
State	Published - Jan 1 2018
Externally published	Yes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2021-02-09

Cite this

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title = "Symmetric variational autoencoder and connections to adversarial learning",

abstract = "A new form of the variational autoencoder (VAE) is proposed, based on the symmetric Kullback-Leibler divergence. It is demonstrated that learning of the resulting symmetric VAE (sVAE) has close connections to previously developed adversarial-learning methods. This relationship helps unify the previously distinct techniques of VAE and adversarially learning, and provides insights that allow us to ameliorate shortcomings with some previously developed adversarial methods. In addition to an analysis that motivates and explains the sVAE, an extensive set of experiments validate the utility of the approach.",

author = "Liqun Chen and Shuyang Dai and Yunchen Pu and Erjin Zhou and Chunyuan Li and Qinliang Su and Changyou Chen and Lawrence Carin",

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AU - Chen, Liqun

AU - Dai, Shuyang

AU - Pu, Yunchen

AU - Zhou, Erjin

AU - Li, Chunyuan

AU - Su, Qinliang

AU - Chen, Changyou

AU - Carin, Lawrence

N1 - Generated from Scopus record by KAUST IRTS on 2021-02-09

PY - 2018/1/1

Y1 - 2018/1/1

N2 - A new form of the variational autoencoder (VAE) is proposed, based on the symmetric Kullback-Leibler divergence. It is demonstrated that learning of the resulting symmetric VAE (sVAE) has close connections to previously developed adversarial-learning methods. This relationship helps unify the previously distinct techniques of VAE and adversarially learning, and provides insights that allow us to ameliorate shortcomings with some previously developed adversarial methods. In addition to an analysis that motivates and explains the sVAE, an extensive set of experiments validate the utility of the approach.

AB - A new form of the variational autoencoder (VAE) is proposed, based on the symmetric Kullback-Leibler divergence. It is demonstrated that learning of the resulting symmetric VAE (sVAE) has close connections to previously developed adversarial-learning methods. This relationship helps unify the previously distinct techniques of VAE and adversarially learning, and provides insights that allow us to ameliorate shortcomings with some previously developed adversarial methods. In addition to an analysis that motivates and explains the sVAE, an extensive set of experiments validate the utility of the approach.

UR - http://www.scopus.com/inward/record.url?scp=85064505992&partnerID=8YFLogxK

M3 - Conference contribution

SP - 661

EP - 669

BT - International Conference on Artificial Intelligence and Statistics, AISTATS 2018

PB - PMLR

ER -

Symmetric variational autoencoder and connections to adversarial learning

Abstract

Bibliographical note

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