Generative Adversarial Networks: A Survey and Taxonomy

Generative adversarial networks (GANs) have been extensively studied in the past few years. Arguably the revolutionary techniques are in the area of computer vision such as plausible image generation, image to image translation, facial attribute manipulation and similar domains. Despite the significant success achieved in the computer vision field, applying GANs to real-world problems still poses significant challenges, three of which we focus on here: (1) High quality image generation; (2) Diverse image generation; and (3) Stable training. Through an in-depth review of GAN-related research in the literature, we provide an account of the architecture-variants and loss-variants, which have been proposed to handle these three challenges from two perspectives. We propose loss-variants and architecture-variants for classifying the most popular GANs, and discuss the potential improvements with focusing on these two aspects. While several reviews for GANs have been presented to date, none have focused on the review of GAN-variants based on their handling the challenges mentioned above. In this paper, we review and critically discuss 7 architecture-variant GANs and 9 loss-variant GANs for remedying those three challenges. The objective of this review is to provide an insight on the footprint that current GANs research focuses on the performance improvement. Code related to GAN-variants studied in this work is summarized on https://github.com/sheqi/GAN_Review.