# Unsupervised Domain Adaptation by Uncertain Feature Alignment

arXiv: https://arxiv.org/abs/2009.06483 · HTML: https://mlsanity.com/paper/2009.06483/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2020.

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

- [Uncertainty-guided Source-free Domain Adaptation](https://mlsanity.com/paper/2208.07591/) (2022)
- [Synergizing between Self-Training and Adversarial Learning for Domain Adaptive Object Detection](https://mlsanity.com/paper/2110.00249/) (2021)
- [Domain Adaptive Object Detection via Balancing Between Self-Training and Adversarial Learning](https://mlsanity.com/paper/2311.04815/) (2023)
- [UBR2S: Uncertainty-Based Resampling and Reweighting Strategy for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2110.11739/) (2021)
- [Certainty Volume Prediction for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2111.02901/) (2021)

## Abstract (arXiv)

Unsupervised domain adaptation (UDA) deals with the adaptation of models from a given source domain with labeled data to an unlabeled target domain. In this paper, we utilize the inherent prediction uncertainty of a model to accomplish the domain adaptation task. The uncertainty is measured by Monte-Carlo dropout and used for our proposed Uncertainty-based Filtering and Feature Alignment (UFAL) that combines an Uncertain Feature Loss (UFL) function and an Uncertainty-Based Filtering (UBF) approach for alignment of features in Euclidean space. Our method surpasses recently proposed architectures and achieves state-of-the-art results on multiple challenging datasets. Code is available on the project website.