  Image generation with explicit condition or label generally works better than
unconditional methods. 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 the 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 if it's
properly modeled. This alleviates the burden of making noise-free labels and
allows the use of weakly-supervised labels in image generation. The second is
that we can use any kinds of weak labels or image features that have
correlations with the original image data to enhance unconditional image
generation. We will show the effectiveness of our joint formulation on CIFAR10,
CIFAR100, and STL dataset with the state-of-the-art GAN architecture.
