# Structured Prediction using cGANs with Fusion Discriminator

Source: [Structured Prediction using cGANs with Fusion Discriminator](<https://www.sota2.com/research/paper/68fa65bc1e209131cea0bfdc>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:33:37.375Z

Authors: Faisal Mahmood, Wenhao Xu, Nicholas J. Durr, Jeremiah W. Johnson, Alan Yuille

Published: 2019-04-30T16:42:55.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1904.13358>)

[PDF](<https://arxiv.org/pdf/1904.13358.pdf>)