Training Domain

Training domain research focuses on improving the generalization ability of machine learning models, particularly deep learning models, to unseen data distributions or domains different from those used during training. Current efforts concentrate on techniques like adversarial training, data augmentation strategies (including stylization and destylization), and risk distribution matching to enhance model robustness and reduce domain shift effects. This research is crucial for deploying machine learning models in real-world applications where data distributions are inherently variable and obtaining sufficient labeled data for every possible scenario is impractical, impacting fields like autonomous driving and medical image analysis.