Teacher-Supervised Generative Adversarial Networks

Although generative adversarial networks (GANs) show impressive effects on image generation, existing GANs suffer an unstable training process, and thus result in poor image quality sometimes. To solve this problem, we first introduce a supervision mechanism into GANs and propose a teacher-supervised GAN (GAN-T) model. Specifically, we design a teacher supervision mechanism to inspect whether the features of generated images are as real as those of real images. If not, we add action into the generator. The action takes the encoding of the real image as prior knowledge to guide the generation of samples. We then apply our proposed method to existing GANs to show its compatibility with them. Finally, we conduct extensive experiments on the tasks of noise-to-image generation and image translation, and experimental results show that our proposed method can significantly stabilize the training process of the generator and improve the quality of generated images.