Semantic Segmentation using Adversarial Networks

Understand

Adversarial training has been shown to produce state of the art results for generative image modeling.

• In this paper we propose an adversarial training approach to train semantic segmentation models.
• We train a convolutional semantic segmentation network along with an adversarial network that discriminates segmentation maps coming either from the ground truth or from the segmentation network.
• The motivation for our approach is that it can detect and correct higher-order inconsistencies between ground truth segmentation maps and the ones produced by the segmentation net.