Symmetric Variational Autoencoder and Connections to Adversarial Learning
Publication
, Conference
Chen, L; Dai, S; Pu, Y; Zhou, E; Li, C; Su, Q; Chen, C; Carin, L
Published in: Proceedings of Machine Learning Research
January 1, 2018
A new form of the variational autoencoder (VAE) is proposed, based on the symmetric KullbackLeibler 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.
Duke Scholars
Published In
Proceedings of Machine Learning Research
EISSN
2640-3498
Publication Date
January 1, 2018
Volume
84
Citation
APA
Chicago
ICMJE
MLA
NLM
Chen, L., Dai, S., Pu, Y., Zhou, E., Li, C., Su, Q., … Carin, L. (2018). Symmetric Variational Autoencoder and Connections to Adversarial Learning. In Proceedings of Machine Learning Research (Vol. 84).
Chen, L., S. Dai, Y. Pu, E. Zhou, C. Li, Q. Su, C. Chen, and L. Carin. “Symmetric Variational Autoencoder and Connections to Adversarial Learning.” In Proceedings of Machine Learning Research, Vol. 84, 2018.
Chen L, Dai S, Pu Y, Zhou E, Li C, Su Q, et al. Symmetric Variational Autoencoder and Connections to Adversarial Learning. In: Proceedings of Machine Learning Research. 2018.
Chen, L., et al. “Symmetric Variational Autoencoder and Connections to Adversarial Learning.” Proceedings of Machine Learning Research, vol. 84, 2018.
Chen L, Dai S, Pu Y, Zhou E, Li C, Su Q, Chen C, Carin L. Symmetric Variational Autoencoder and Connections to Adversarial Learning. Proceedings of Machine Learning Research. 2018.
Published In
Proceedings of Machine Learning Research
EISSN
2640-3498
Publication Date
January 1, 2018
Volume
84