[1906.01529] 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 computer vision field, applying GANs over real-world problems still have three main challenges: (1) High quality image generation; (2) Diverse image generation; and (3) Stable training. Considering numerous GAN-related research in the literature, we provide a study on the architecture-variants and loss-variants, which are proposed to handle these three challenges from two perspectives. We propose loss and architecture-variants for classifying most popular GANs, and discuss the potential improvements with focusing on these two aspects. While several reviews for GANs have been presented, there is no work focusing on the review of GAN-variants based on handling challenge

2 mentions: @wangvilla@FelixEVargas
Date: 2019/06/06 02:17

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@wangvilla Generative Adversarial Networks: A Survey and Taxonomy t.co/498CtHsYJG. Review the recent development of GANs based on their losses and architectures. #GANs #AI

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