An Edge Detection Technique for Performance Improvement of eGAN


KIPS Transactions on Software and Data Engineering, Vol. 10, No. 3, pp. 109-114, Mar. 2021
https://doi.org/10.3745/KTSDE.2021.10.3.109,   PDF Download:
Keywords: Generative Adversarial Network, Loss Function, Edge detection, eGAN
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.


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Cite this article
[IEEE Style]
L. C. Youn, J. S. Park, J. G. Shon, "An Edge Detection Technique for Performance Improvement of eGAN," KIPS Transactions on Software and Data Engineering, vol. 10, no. 3, pp. 109-114, 2021. DOI: https://doi.org/10.3745/KTSDE.2021.10.3.109.

[ACM Style]
Lee Cho Youn, Ji Su Park, and Jin Gon Shon. 2021. An Edge Detection Technique for Performance Improvement of eGAN. KIPS Transactions on Software and Data Engineering, 10, 3, (2021), 109-114. DOI: https://doi.org/10.3745/KTSDE.2021.10.3.109.