# A Fully Convolutional Tri-branch Network (FCTN) for Domain Adaptation

arXiv: https://arxiv.org/abs/1711.03694 · HTML: https://mlsanity.com/paper/1711.03694/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2017.

## Built on by (most cited first, 4 of 4)

- [Cross-View Regularization for Domain Adaptive Panoptic Segmentation](https://mlsanity.com/paper/2103.02584/) (2021)
- [Randomized Histogram Matching: A Simple Augmentation for Unsupervised Domain Adaptation in Overhead Imagery](https://mlsanity.com/paper/2104.14032/) (2021)
- [Exploiting Image Translations via Ensemble Self-Supervised Learning for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2107.06235/) (2021)
- [Distribution Regularized Self-Supervised Learning for Domain Adaptation of Semantic Segmentation](https://mlsanity.com/paper/2206.09683/) (2022)

## Abstract (arXiv)

A domain adaptation method for urban scene segmentation is proposed in this work. We develop a fully convolutional tri-branch network, where two branches assign pseudo labels to images in the unlabeled target domain while the third branch is trained with supervision based on images in the pseudo-labeled target domain. The re-labeling and re-training processes alternate. With this design, the tri-branch network learns target-specific discriminative representations progressively and, as a result, the cross-domain capability of the segmenter improves. We evaluate the proposed network on large-scale domain adaptation experiments using both synthetic (GTA) and real (Cityscapes) images. It is shown that our solution achieves the state-of-the-art performance and it outperforms previous methods by a significant margin.