  We propose a simple yet highly effective method that addresses the
mode-collapse problem in the Conditional Generative Adversarial Network (cGAN).
Although conditional distributions are multi-modal (i.e., having many modes) in
practice, most cGAN approaches tend to learn an overly simplified distribution
where an input is always mapped to a single output regardless of variations in
latent code. To address such issue, we propose to explicitly regularize the
generator to produce diverse outputs depending on latent codes. The proposed
regularization is simple, general, and can be easily integrated into most
conditional GAN objectives. Additionally, explicit regularization on generator
allows our method to control a balance between visual quality and diversity. We
demonstrate the effectiveness of our method on three conditional generation
tasks: image-to-image translation, image inpainting, and future video
prediction. We show that simple addition of our regularization to existing
models leads to surprisingly diverse generations, substantially outperforming
the previous approaches for multi-modal conditional generation specifically
designed in each individual task.
