VAE Learning via Stein Variational Gradient Descent

被引：0

作者：

Pu, Yunchen ^{[1
]}

Gan, Zhe ^{[1
]}

Henao, Ricardo ^{[1
]}

Li, Chunyuan ^{[1
]}

Han, Shaobo ^{[1
]}

Carin, Lawrence ^{[1
]}

机构：

[1] Duke Univ, Dept Elect & Comp Engn, Durham, NC 27706 USA

来源：

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 30 (NIPS 2017) | 2017年 / 30卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A new method for learning variational autoencoders (VAEs) is developed, based on Stein variational gradient descent. A key advantage of this approach is that one need not make parametric assumptions about the form of the encoder distribution. Performance is further enhanced by integrating the proposed encoder with importance sampling. Excellent performance is demonstrated across multiple unsupervised and semi-supervised problems, including semi-supervised analysis of the ImageNet data, demonstrating the scalability of the model to large datasets.

引用

页数：10

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