  Recent deep learning based approaches have shown remarkable success on object
segmentation tasks. However, there is still room for further improvement.
Inspired by generative adversarial networks, we present a generic end-to-end
adversarial approach, which can be combined with a wide range of existing
semantic segmentation networks to improve their segmentation performance. The
key element of our method is to replace the commonly used binary adversarial
loss with a high resolution pixel-wise loss. In addition, we train our
generator employing stochastic weight averaging fashion, which further enhances
the predicted output label maps leading to state-of-the-art results. We show,
that this combination of pixel-wise adversarial training and weight averaging
leads to significant and consistent gains in segmentation performance, compared
to the baseline models.
