Transformation of Identity-Preserved Facial Features using Wasserstein Generative Adversarial Network with Gradient Penalty
碩士 === 國立臺灣科技大學 === 機械工程系 === 106 === We propose the Disentangled Representation Learning on a Wasserstein Generative Adversarial Network with Gradient Penalty, or abbreviated as the DR-WGAN, for handling cross-pose face recognition. The proposed DR-WGAN has improved the state-of-the-art DR-GAN (Dis...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Others |
| Language: | zh-TW |
| Published: |
2018
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| Online Access: | http://ndltd.ncl.edu.tw/handle/242r48 |