# Unsupervised Domain Adaptation using Feature-Whitening and Consensus Loss

arXiv: https://arxiv.org/abs/1903.03215 · HTML: https://mlsanity.com/paper/1903.03215/

## Key facts

- Cited by 67 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2019.

## Built on by (most cited first, 30 of 67)

- [RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening](https://mlsanity.com/paper/2103.15597/) (2021)
- [The Unreasonable Effectiveness of Entropy Minimization in LLM Reasoning](https://mlsanity.com/paper/2505.15134/) (2025)
- [On Feature Decorrelation in Self-Supervised Learning](https://mlsanity.com/paper/2105.00470/) (2021)
- [Unsupervised Domain Adaptation of Object Detectors: A Survey](https://mlsanity.com/paper/2105.13502/) (2021)
- [WildNet: Learning Domain Generalized Semantic Segmentation from the Wild](https://mlsanity.com/paper/2204.01446/) (2022)
- [Factors of Influence for Transfer Learning across Diverse Appearance Domains and Task Types](https://mlsanity.com/paper/2103.13318/) (2021)
- [A Theory of Label Propagation for Subpopulation Shift](https://mlsanity.com/paper/2102.11203/) (2021)
- [Uncertainty-guided Source-free Domain Adaptation](https://mlsanity.com/paper/2208.07591/) (2022)
- [In Search of Lost Online Test-time Adaptation: A Survey](https://mlsanity.com/paper/2310.20199/) (2023)
- [Your Classifier can Secretly Suffice Multi-Source Domain Adaptation](https://mlsanity.com/paper/2103.11169/) (2021)
- [Instance-Aware Domain Generalization for Face Anti-Spoofing](https://mlsanity.com/paper/2304.05640/) (2023)
- [Curriculum Graph Co-Teaching for Multi-Target Domain Adaptation](https://mlsanity.com/paper/2104.00808/) (2021)
- [A Survey of Unsupervised Domain Adaptation for Visual Recognition](https://mlsanity.com/paper/2112.06745/) (2021)
- [Leveraging Self-Supervision for Cross-Domain Crowd Counting](https://mlsanity.com/paper/2103.16291/) (2021)
- [Generalized Domain Conditioned Adaptation Network](https://mlsanity.com/paper/2103.12339/) (2021)
- [Compositional Semantic Mix for Domain Adaptation in Point Cloud Segmentation](https://mlsanity.com/paper/2308.14619/) (2023)
- [Listen, Adapt, Better WER: Source-free Single-utterance Test-time Adaptation for Automatic Speech Recognition](https://mlsanity.com/paper/2203.14222/) (2022)
- [VNE: An Effective Method for Improving Deep Representation by Manipulating Eigenvalue Distribution](https://mlsanity.com/paper/2304.01434/) (2023)
- [Limitations of Post-Hoc Feature Alignment for Robustness](https://mlsanity.com/paper/2103.05898/) (2021)
- [Transferrable Contrastive Learning for Visual Domain Adaptation](https://mlsanity.com/paper/2112.07516/) (2021)
- [Towards Robust and Efficient Cloud-Edge Elastic Model Adaptation via Selective Entropy Distillation](https://mlsanity.com/paper/2402.17316/) (2024)
- [Unsupervised Domain Adaptation for Video Transformers in Action Recognition](https://mlsanity.com/paper/2207.12842/) (2022)
- [Weak Adaptation Learning -- Addressing Cross-domain Data Insufficiency with Weak Annotator](https://mlsanity.com/paper/2102.07358/) (2021)
- [Learning Across Decentralized Multi-Modal Remote Sensing Archives with Federated Learning](https://mlsanity.com/paper/2306.00792/) (2023)
- [Details Matter for Indoor Open-vocabulary 3D Instance Segmentation](https://mlsanity.com/paper/2507.23134/) (2025)
- [Exploiting Both Domain-specific and Invariant Knowledge via a Win-win Transformer for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2111.12941/) (2021)
- [Improving Pseudo Labels With Intra-Class Similarity for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2207.12139/) (2022)
- [Making the Best of Both Worlds: A Domain-Oriented Transformer for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2208.01195/) (2022)
- [Domain-Generalizable Multiple-Domain Clustering](https://mlsanity.com/paper/2301.13530/) (2023)
- [Model Whisper: Steering Vectors Unlock Large Language Models' Potential in Test-time](https://mlsanity.com/paper/2512.04748/) (2025)

## Abstract (arXiv)

A classifier trained on a dataset seldom works on other datasets obtained under different conditions due to domain shift. This problem is commonly addressed by domain adaptation methods. In this work we introduce a novel deep learning framework which unifies different paradigms in unsupervised domain adaptation. Specifically, we propose domain alignment layers which implement feature whitening for the purpose of matching source and target feature distributions. Additionally, we leverage the unlabeled target data by proposing the Min-Entropy Consensus loss, which regularizes training while avoiding the adoption of many user-defined hyper-parameters. We report results on publicly available datasets, considering both digit classification and object recognition tasks. We show that, in most of our experiments, our approach improves upon previous methods, setting new state-of-the-art performances.