ENTROPY-REGULARIZED OPTIMAL TRANSPORT GENERATIVE MODELS

被引：0

作者：

Liu, Dong ^{[1
]}

Minh Thanh Vu ^{[1
]}

Chatterjee, Saikat ^{[1
]}

Rasmussen, Lars K. ^{[1
]}

机构：

[1] KTH Royal Inst Technol, Stockholm, Sweden

来源：

2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2019年

关键词：

Optimal transport; generative models;

D O I：

10.1109/icassp.2019.8682721

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

We investigate the use of entropy-regularized optimal transport (EOT) cost in developing generative models to learn implicit distributions. Two generative models are proposed. One uses EOT cost directly in an one-shot optimization problem and the other uses EOT cost iteratively in an adversarial game. The proposed generative models show improved performance over contemporary models on scores of sample based test.

引用

页码：3532 / 3536

页数：5

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