Generative adversarial networks

Yang Ye

doi:10.1117/12.2626949

2 March 2022 Generative adversarial networks

Yang Ye

Proceedings Volume 12158, International Conference on Computer Vision and Pattern Analysis (ICCPA 2021); 121580T (2022) https://doi.org/10.1117/12.2626949
Event: 2021 International Conference on Computer Vision and Pattern Analysis, 2021, Guangzhou, China

Abstract

Generative Adversarial Networks has shown impressive achievement in computer graphics applications, and it's now widely used worldwide. GAN is composed of generator and discriminator, which are crucial compositions of GAN. GAN can generate 3D models, graphics that are required in animated movies or video game characters. For example, a generative model could simply generate a new picture that looks like a specific type of animal. In the meantime, the discriminative model could distinguish the picture from a human being or an animal. GAN variants consist of progressive GAN as well as conditional GAN. Since GAN can be applied in so many different areas, in this essay, we are going to talk about how GAN is applied in image and computer vision. More specifically, face synthesis, image to image translation, and super resolution. Conclusively, GAN has a significant contribution to various areas, and it boosts the advancement in the domain of computer graphics.

Citation Download Citation

Yang Ye "Generative adversarial networks", Proc. SPIE 12158, International Conference on Computer Vision and Pattern Analysis (ICCPA 2021), 121580T (2 March 2022); https://doi.org/10.1117/12.2626949

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