A Brief Analysis of GAN Variants on Image Classification and Generation

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Recently, the Generative Adversarial Network (GAN) has attracted much attention in a large range of applications, such as image classification, plausible image generation and image completion. Lots of GAN variants have been proposed. This paper focused on two popular GAN variants, including GAN and Auxiliary Classifier Generative Adversarial Network (ACGAN) and made a comparison between them. The experiment on CIFAR-10 and anima dataset shows that ACGAN can perform better than original one with better accuracy. Furthermore, we find ACGAN has a worse inception score (IS), which indicates that ACGAN is not capable of generating realistic pictures.