# MixMatch Domain Adaptaion: Prize-winning solution for both tracks of VisDA 2019 challenge

arXiv: https://arxiv.org/abs/1910.03903 · HTML: https://mlsanity.com/paper/1910.03903/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2019.

## Built on by (most cited first, 5 of 5)

- [ProxyMix: Proxy-based Mixup Training with Label Refinery for Source-Free Domain Adaptation](https://mlsanity.com/paper/2205.14566/) (2022)
- [Semi-supervised Domain Adaptation based on Dual-level Domain Mixing for Semantic Segmentation](https://mlsanity.com/paper/2103.04705/) (2021)
- [Semi-supervised Domain Adaptation for Semantic Segmentation](https://mlsanity.com/paper/2110.10639/) (2021)
- [Domain Adaptation and Active Learning for Fine-Grained Recognition in the Field of Biodiversity](https://mlsanity.com/paper/2110.11778/) (2021)
- [GeT: Generative Target Structure Debiasing for Domain Adaptation](https://mlsanity.com/paper/2308.10205/) (2023)

## Abstract (arXiv)

We present a domain adaptation (DA) system that can be used in multi-source and semi-supervised settings. Using the proposed method we achieved 2nd place on multi-source track and 3rd place on semi-supervised track of the VisDA 2019 challenge (http://ai.bu.edu/visda-2019/). The source code of the method is available at https://github.com/filaPro/visda2019.