Single Source Domain Generalization

Single-source domain generalization (SSDG) tackles the challenge of training machine learning models, particularly deep learning models, on data from a single source domain to achieve robust performance on unseen, different domains. Current research focuses on developing data augmentation techniques, often leveraging frequency-domain manipulations or generative models, and incorporating methods like contrastive learning, adversarial training, and causal inference to learn domain-invariant features. This area is crucial for applications like medical image analysis where acquiring diverse datasets is difficult due to data scarcity and privacy concerns, enabling the development of more generalizable and reliable diagnostic tools.