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.

翻译：最近,基于基因对抗网络(GANs)的半监督的学习方法受到高度重视,其中两种不同方法在各种基准数据集上取得了竞争性结果。Bad GAN学会了一种分类方法,在输入数据支持的辅助部分中散发了不切实际的样本。相反,Triple GAN由三人游戏组成,试图利用良好生成的样本来提高分类结果。在本文中,我们对这两种方法对不同的基准数据集进行了综合比较。我们展示了它们对于图像生成的不同属性,以及对所提供标签数据的数量的敏感性。通过全面比较这两种方法,我们希望能够揭示以GAN为基础的半监督学习的未来。