Robust Checkpoint Selection by Exponential Moving Averaging for Domain Generalized Segmentation

Deep learning has seen significant progress in applications such as autonomous driving and healthcare, with synthetic data playing an increasingly important role. However, models trained on synthetic data often suffer performance drops in real-world settings due to domain shifts. Domain Generalization (DG) aims to address this problem by developing models that can robustly generalize to unseen domains, with a key challenge being the selection of an appropriate checkpoint. For the checkpoint selection problem, we introduce a standardized approach that simultaneously enhances model robustness through the combination of Exponential Moving Average (EMA) and Data Augmentation. Our method reduces performance variability and improves generalization in 25 out of 26 experiments, highlighting EMA as a promising technique for more stable and reliable DG performance in real-world applications.