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http://dx.doi.org/10.3745/KTSDE.2021.10.3.109

An Edge Detection Technique for Performance Improvement of eGAN  

Lee, Cho Youn (한국방송통신대학원 정보과학과)
Park, Ji Su (전주대학교 컴퓨터공학과)
Shon, Jin Gon (한국방송통신대학원 컴퓨터과학과)
Publication Information
KIPS Transactions on Software and Data Engineering / v.10, no.3, 2021 , pp. 109-114 More about this Journal
Abstract
GAN(Generative Adversarial Network) is an image generation model, which is composed of a generator network and a discriminator network, and generates an image similar to a real image. Since the image generated by the GAN should be similar to the actual image, a loss function is used to minimize the loss error of the generated image. However, there is a problem that the loss function of GAN degrades the quality of the image by making the learning to generate the image unstable. To solve this problem, this paper analyzes GAN-related studies and proposes an edge GAN(eGAN) using edge detection. As a result of the experiment, the eGAN model has improved performance over the existing GAN model.
Keywords
Generative Adversarial Network; Loss Function; Edge Detection; eGAN;
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