Domain Generalization

Domain generalization (DG) aims to train machine learning models that perform well on unseen data, overcoming the limitations of models trained and tested on similar data distributions. Current research focuses on improving model robustness through techniques like self-supervised learning, data augmentation (including novel methods like style prompting and spectrum synthesis), and the use of foundation models and parameter-efficient fine-tuning. These advancements are crucial for deploying reliable AI systems in real-world applications where data variability is inevitable, particularly in fields like medical imaging, autonomous systems, and natural language processing.