  Conditional Generative Adversarial Networks are known to be difficult to
train, especially when the conditions are continuous and high-dimensional. To
partially alleviate this difficulty, we propose a simple generator
regularization term on the GAN generator loss in the form of Lipschitz penalty.
Thus, when the generator is fed with neighboring conditions in the continuous
space, the regularization term will leverage the neighbor information and push
the generator to generate samples that have similar conditional distributions
for each neighboring condition. We analyze the effect of the proposed
regularization term and demonstrate its robust performance on a range of
synthetic and real-world tasks.
