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The Ethereal
Entropy-regularized Optimal Transport Generative Models
November 16, 2018 ยท Declared Dead ยท ๐ IEEE International Conference on Acoustics, Speech, and Signal Processing
"No code URL or promise found in abstract"
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Authors
Dong Liu, Minh Thร nh Vu, Saikat Chatterjee, Lars K. Rasmussen
arXiv ID
1811.06763
Category
cs.LG: Machine Learning
Cross-listed
stat.ML
Citations
5
Venue
IEEE International Conference on Acoustics, Speech, and Signal Processing
Last Checked
4 months ago
Abstract
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 for image generation on MNSIT.
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