# Object Segmentation using Pixel-wise Adversarial Loss

arXiv: https://arxiv.org/abs/1909.10341 · HTML: https://mlsanity.com/paper/1909.10341/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2019.

## Built on by (most cited first, 1 of 1)

- [Bayesian Generative Adversarial Networks via Gaussian Approximation for Tabular Data Synthesis](https://mlsanity.com/paper/2602.21948/) (2026)

## Abstract (arXiv)

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.