  Conditional generative adversarial networks (cGAN) have led to large
improvements in the task of conditional image generation, which lies at the
heart of computer vision. The major focus so far has been on performance
improvement, while there has been little effort in making cGAN more robust to
noise. The regression (of the generator) might lead to arbitrarily large errors
in the output, which makes cGAN unreliable for real-world applications. In this
work, we introduce a novel conditional GAN model, called RoCGAN, which
leverages structure in the target space of the model to address the issue. Our
model augments the generator with an unsupervised pathway, which promotes the
outputs of the generator to span the target manifold even in the presence of
intense noise. We prove that RoCGAN share similar theoretical properties as GAN
and experimentally verify that our model outperforms existing state-of-the-art
cGAN architectures by a large margin in a variety of domains including images
from natural scenes and faces.
