# E-MLNet: Enhanced Mutual Learning for Universal Domain Adaptation with Sample-Specific Weighting

arXiv: https://arxiv.org/abs/2509.09006 · HTML: https://mlsanity.com/paper/2509.09006/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 26 references that are in the map.
- First posted on arXiv 10 Sep 2025.

## Builds on (most cited first, 16 of 16)

- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Deep Hashing Network for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1706.07522/) (2017)
- [Deep Anomaly Detection with Outlier Exposure](https://mlsanity.com/paper/1812.04606/) (2018)
- [VisDA: The Visual Domain Adaptation Challenge](https://mlsanity.com/paper/1710.06924/) (2017)
- [Universal Domain Adaptation through Self Supervision](https://mlsanity.com/paper/2002.07953/) (2020)
- [Open Set Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1804.10427/) (2018)
- [OVANet: One-vs-All Network for Universal Domain Adaptation](https://mlsanity.com/paper/2104.03344/) (2021)
- [On the Effectiveness of Image Rotation for Open Set Domain Adaptation](https://mlsanity.com/paper/2007.12360/) (2020)
- [Unified Optimal Transport Framework for Universal Domain Adaptation](https://mlsanity.com/paper/2210.17067/) (2022)
- [Open Set Domain Adaptation: Theoretical Bound and Algorithm](https://mlsanity.com/paper/1907.08375/) (2019)
- [Self-Calibrated Tuning of Vision-Language Models for Out-of-Distribution Detection](https://mlsanity.com/paper/2411.03359/) (2024)
- [MLNet: Mutual Learning Network with Neighborhood Invariance for Universal Domain Adaptation](https://mlsanity.com/paper/2312.07871/) (2023)
- [Learning Classifiers of Prototypes and Reciprocal Points for Universal Domain Adaptation](https://mlsanity.com/paper/2212.08355/) (2022)
- [Improving Transferability of Domain Adaptation Networks Through Domain Alignment Layers](https://mlsanity.com/paper/2109.02693/) (2021)
- [Tightening Classification Boundaries in Open Set Domain Adaptation through Unknown Exploitation](https://mlsanity.com/paper/2309.08964/) (2023)
- [Beyond the Known: Enhancing Open Set Domain Adaptation with Unknown Exploration](https://mlsanity.com/paper/2412.18105/) (2024)

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

- [Joint Distribution Alignment for Universal Domain Adaptation](https://mlsanity.com/paper/2608.24429/) (2026)

## Abstract (arXiv)

Universal Domain Adaptation (UniDA) seeks to transfer knowledge from a labeled source to an unlabeled target domain without assuming any relationship between their label sets, requiring models to classify known samples while rejecting unknown ones. Advanced methods like Mutual Learning Network (MLNet) use a bank of one-vs-all classifiers adapted via Open-set Entropy Minimization (OEM). However, this strategy treats all classifiers equally, diluting the learning signal. We propose the Enhanced Mutual Learning Network (E-MLNet), which integrates a dynamic weighting strategy to OEM. By leveraging the closed-set classifier's predictions, E-MLNet focuses adaptation on the most relevant class boundaries for each target sample, sharpening the distinction between known and unknown classes. We conduct extensive experiments on four challenging benchmarks: Office-31, Office-Home, VisDA-2017, and ImageCLEF. The results demonstrate that E-MLNet achieves the highest average H-scores on VisDA and ImageCLEF and exhibits superior robustness over its predecessor. E-MLNet outperforms the strong MLNet baseline in the majority of individual adaptation tasks -- 22 out of 31 in the challenging Open-Partial DA setting and 19 out of 31 in the Open-Set DA setting -- confirming the benefits of our focused adaptation strategy.