Related papers: Generative Adversarial Networks

Generative Adversarial Networks

URL: http://arxiv.org/abs/2203.00667v1
Date: Tue, 1 Mar 2022 18:37:48 GMT
Title: Generative Adversarial Networks
Authors: Gilad Cohen and Raja Giryes
Abstract summary: Generative Adversarial Networks (GANs) are very popular frameworks for generating high-quality data. This chapter gives an introduction to GANs, by discussing their principle mechanism and presenting some of their inherent problems during training and evaluation.
Score: 43.10140199124212
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Generative Adversarial Networks (GANs) are very popular frameworks for generating high-quality data, and are immensely used in both the academia and industry in many domains. Arguably, their most substantial impact has been in the area of computer vision, where they achieve state-of-the-art image generation. This chapter gives an introduction to GANs, by discussing their principle mechanism and presenting some of their inherent problems during training and evaluation. We focus on these three issues: (1) mode collapse, (2) vanishing gradients, and (3) generation of low-quality images. We then list some architecture-variant and loss-variant GANs that remedy the above challenges. Lastly, we present two utilization examples of GANs for real-world applications: Data augmentation and face images generation.

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