Structured Prediction using cGANs with Fusion Discriminator

ICLR 2019 Faisal MahmoodWenhao XuNicholas J. DurrJeremiah W. JohnsonAlan Yuille

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... (read more)

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