JGAN: A Joint Formulation of GAN for Synthesizing Images and Labels

Image generation with explicit condition or label generally works better than unconditional image generation. In modern GAN frameworks, both generator and discriminator are formulated to model the conditional distribution of images given with labels. In this paper, we provide an alternative formulation of GAN which models joint distribution of images and labels. There are two advantages in this joint formulation over conditional approaches. The first advantage is that the joint formulation is more robust to label noises, and the second is we can use any kind of weak labels (or additional information which has dependence on the original image data) to enhance unconditional image generation. We will show the effectiveness of joint formulation in CIFAR-10, CIFAR-100, and STL dataset.