Structured Prediction using cGANs with Fusion Discriminator

Conference Paper

Abstract

  • 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.
  • Authors

  • Mahmood, Faisal
  • Xu, Wenhao
  • Durr, Nicholas J
  • Johnson, Jeremiah
  • Yuille, Alan
  • Status

    Publication Date

  • May 2, 2019
  • Keywords

  • cs.CV
  • cs.LG
  • eess.IV