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JBE, vol. 27, no. 3, pp.437-450, May, 2022


Deep Learning-based Single Image Generative Adversarial Network: Performance Comparison and Trends

Seong-Hun Jeong and Kyeongbo Kong

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Generative adversarial networks(GANs) have demonstrated remarkable success in image synthesis. However, since GANs show instability in the training stage on large datasets, it is difficult to apply to various application fields. A single image GAN is a field that generates various images by learning the internal distribution of a single image. In this paper, we investigate five Single Image GAN: SinGAN, ConSinGAN, InGAN, DeepSIM, and One-Shot GAN. We compare the performance of each model and analyze the pros and cons of a single image GAN.

Keyword: Generative adversarial network, single image, image manipulation, image retargeting

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