Semi-supervised learning based on generative adversarial network: a comparison between good GAN and bad GAN approach
This is an incremental comparison study for researchers in semi-supervised learning and GANs.
The paper compared two semi-supervised learning methods based on generative adversarial networks (GANs), Bad GAN and Triple GAN, on benchmark datasets, showing their different properties in image generation and sensitivity to labeled data amounts.
Recently, semi-supervised learning methods based on generative adversarial networks (GANs) have received much attention. Among them, two distinct approaches have achieved competitive results on a variety of benchmark datasets. Bad GAN learns a classifier with unrealistic samples distributed on the complement of the support of the input data. Conversely, Triple GAN consists of a three-player game that tries to leverage good generated samples to boost classification results. In this paper, we perform a comprehensive comparison of these two approaches on different benchmark datasets. We demonstrate their different properties on image generation, and sensitivity to the amount of labeled data provided. By comprehensively comparing these two methods, we hope to shed light on the future of GAN-based semi-supervised learning.