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Paper Abstract and Keywords
Presentation 2020-02-28 15:10
Unpaired Learning for Noise-free, Scale Invariant, and Interpretable Image Enhancement
Satoshi Kosugi, Toshihiko Yamasaki (Univ. of Tokyo)
Abstract (in Japanese) (See Japanese page) 
(in English) This paper tackles unpaired image enhancement, a task of learning a mapping function which transforms input images into enhanced images in the absence of input-output image pairs. Our method is based on generative adversarial networks (GANs), but instead of simply generating images with a neural network, we enhance images utilizing image editing software to achieve noise-free, scale invariant, and interpretable image enhancement. To incorporate image editing software into a GAN, we propose a reinforcement learning framework. We apply the proposed method to photo enhancement and face beautification and demonstrate that the proposed method achieves the best performance.
Keyword (in Japanese) (See Japanese page) 
(in English) image enhancement / unpaired learning / reinforcement learning / generative adversarial network / / / /  
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Conference Information
Committee HI IEICE-IE IEICE-ITS MMS ME AIT  
Conference Date 2020-02-27 - 2020-02-28 
Place (in Japanese) (See Japanese page) 
Place (in English) Hokkaido Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Image Processing, etc. 
Paper Information
Registration To IEICE-IE 
Conference Code 2020-02-HI-IE-ITS-MMS-ME-AIT 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Unpaired Learning for Noise-free, Scale Invariant, and Interpretable Image Enhancement 
Sub Title (in English)  
Keyword(1) image enhancement  
Keyword(2) unpaired learning  
Keyword(3) reinforcement learning  
Keyword(4) generative adversarial network  
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1st Author's Name Satoshi Kosugi  
1st Author's Affiliation the University of Tokyo (Univ. of Tokyo)
2nd Author's Name Toshihiko Yamasaki  
2nd Author's Affiliation the University of Tokyo (Univ. of Tokyo)
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Speaker Author-1 
Date Time 2020-02-28 15:10:00 
Presentation Time 15 minutes 
Registration for IEICE-IE 
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Volume (vol) vol.44 
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