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.

这篇论文提出了一种简单而高效的方法来解决条件生成式对抗网络（cGAN）中的模式崩塌问题，它通过显式地规范化生成器以产生不同的输出来控制可变因素，从而在视觉质量和多样性之间实现平衡，这种方法在图像翻译、图像修补、未来预测等多个条件生成任务中取得了出色的效果。