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研究生: 張明山
Richardo Tiono
論文名稱: Original Face Color Recovery via Adversarial Network
Original Face Color Recovery via Adversarial Network
指導教授: 姚智原
Chih-Yuan Yao
口試委員: 賴祐吉
Yu-Chi Lai
朱宏國
Hung-Kuo Chu
學位類別: 碩士
Master
系所名稱: 電資學院 - 資訊工程系
Department of Computer Science and Information Engineering
論文出版年: 2020
畢業學年度: 108
語文別: 英文
論文頁數: 47
中文關鍵詞: Machine LearningFace ReconstructionGenerative Adversarial NetworkDeep LearningImage to Image Translation
外文關鍵詞: Machine Learning, Face Reconstruction, Generative Adversarial Network, Deep Learning, Image to Image Translation
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  • Recommendation Letter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i Approval Letter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv Table of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Thesis Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.1 Generative Adversarial Networks . . . . . . . . . . . . . . . . . . . . 4 2.2 Image to image translation . . . . . . . . . . . . . . . . . . . . . . . . 5 2.3 Face Relighting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3 Thesis Overview and Loss Function . . . . . . . . . . . . . . . . . . . . . . 9 3.1 Thesis Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.2 Loss Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.3 Adversarial Loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.4 L1 Loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 3.5 Perceptual Loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 3.6 Full Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 4 Network Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 4.1 Generator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 4.2 Discriminator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 5 Experiment and Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 5.1 Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 5.1.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 5.1.2 System and Hardware Environment . . . . . . . . . . . . . . . 23 5.2 Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 5.2.1 Training Con guration . . . . . . . . . . . . . . . . . . . . . . 23 5.2.2 Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 5.2.2.1 Ablation Studies . . . . . . . . . . . . . . . . . . . . 25 5.2.3 Baseline Comparison . . . . . . . . . . . . . . . . . . . . . . . 27 5.2.3.1 Baselines . . . . . . . . . . . . . . . . . . . . . . . . 27 5.2.3.2 Qualitative Evaluation . . . . . . . . . . . . . . . . . 28 5.2.3.3 Quantitative Evaluation . . . . . . . . . . . . . . . . 30 6 Conclusion and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . 32 6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

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