Semantic Segmentation using Adversarial Networks
Pauline Luc
and
Camille Couprie
and
Soumith Chintala
and
Jakob Verbeek
arXiv e-Print archive - 2016 via Local arXiv
Keywords:
cs.CV
First published: 2016/11/25 (7 years ago) Abstract: 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.
