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

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.