Improving Pseudo-labelling and Enhancing Robustness for Semi-Supervised Domain Generalization
This addresses the challenge of learning cross-domain generalizable models with limited labeled data, which is crucial for real-world applications like automated healthcare, but the work appears incremental as it builds on existing SSL-based SSDG methods.
The paper tackles the problem of Semi-Supervised Domain Generalization (SSDG) by proposing an uncertainty-guided pseudo-labelling with model averaging approach, which improves pseudo-label selection and reduces overfitting, achieving effectiveness against existing methods in experiments on key DG datasets.
Beyond attaining domain generalization (DG), visual recognition models should also be data-efficient during learning by leveraging limited labels. We study the problem of Semi-Supervised Domain Generalization (SSDG) which is crucial for real-world applications like automated healthcare. SSDG requires learning a cross-domain generalizable model when the given training data is only partially labelled. Empirical investigations reveal that the DG methods tend to underperform in SSDG settings, likely because they are unable to exploit the unlabelled data. Semi-supervised learning (SSL) shows improved but still inferior results compared to fully-supervised learning. A key challenge, faced by the best-performing SSL-based SSDG methods, is selecting accurate pseudo-labels under multiple domain shifts and reducing overfitting to source domains under limited labels. In this work, we propose new SSDG approach, which utilizes a novel uncertainty-guided pseudo-labelling with model averaging (UPLM). Our uncertainty-guided pseudo-labelling (UPL) uses model uncertainty to improve pseudo-labelling selection, addressing poor model calibration under multi-source unlabelled data. The UPL technique, enhanced by our novel model averaging (MA) strategy, mitigates overfitting to source domains with limited labels. Extensive experiments on key representative DG datasets suggest that our method demonstrates effectiveness against existing methods. Our code and chosen labelled data seeds are available on GitHub: https://github.com/Adnan-Khan7/UPLM