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.
We propose a simple and general approach that avoids a mode collapse problem in various conditional GANs. The paper proposes a regularization term for the conditional GAN objective in order to promote diverse multimodal generation and prevent mode collapse. The paper proposes a method for generating diverse outputs for various conditional GAN frameworks including image-to-image translation, image-inpainting, and video prediction, which can be applied to various conditional synthesis frameworks for various tasks. 