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. Our experiments show that our adversarial training approach leads to improved accuracy on the Stanford Background and PASCAL VOC 2012 datasets.
In particular, REF estimated a semantic segmentation model via an adversarial process by training a discriminator for distinguishing ground-truths from outputs of the semantic segmentation model and the semantic segmentation model for fooling the discriminator.
Semantic Segmentation using Adversarial Networks
NIPS Workshop on Adversarial Training, Dec 2016, Barcelona, Spain
Computer Science