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

Department of Computer Science and Engineering

University at Buffalo

Duke University
Megvii Research
Sun Yat-Sen University

Research output: Contribution to conference › Paper › peer-review

44 Scopus citations

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
Pages	661-669
Number of pages	9
State	Published - 2018
Event	21st International Conference on Artificial Intelligence and Statistics, AISTATS 2018 - Playa Blanca, Lanzarote, Canary Islands, Spain Duration: Apr 9 2018 → Apr 11 2018

Conference

Conference	21st International Conference on Artificial Intelligence and Statistics, AISTATS 2018
Country/Territory	Spain
City	Playa Blanca, Lanzarote, Canary Islands
Period	04/9/18 → 04/11/18

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",

year = "2018",

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TY - CONF

T1 - Symmetric variational autoencoder and connections to adversarial learning

AU - Chen, Liqun

AU - Dai, Shuyang

AU - Pu, Yunchen

AU - Zhou, Erjin

AU - Li, Chunyuan

AU - Su, Qinliang

AU - Chen, Changyou

AU - Carin, Lawrence

PY - 2018

Y1 - 2018

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 - https://www.scopus.com/pages/publications/85064505992

M3 - Paper

SP - 661

EP - 669

T2 - 21st International Conference on Artificial Intelligence and Statistics, AISTATS 2018

Y2 - 9 April 2018 through 11 April 2018

ER -

Symmetric variational autoencoder and connections to adversarial learning

Abstract

Conference

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