We propose the fusion discriminator, a single unified framework for
incorporating conditional information into a generative adversarial network
(GAN) for a variety of distinct structured prediction tasks, including image
synthesis, semantic segmentation, and depth estimation. Much like commonly used
convolutional neural network -- conditional Markov random field (CNN-CRF)
models, the proposed method is able to enforce higher-order consistency in the
model, but without being limited to a very specific class of potentials. The
method is conceptually simple and flexible, and our experimental results
demonstrate improvement on several diverse structured prediction tasks.
