Single Domain Generalization

Single domain generalization (SDG) tackles the challenge of training machine learning models that generalize well to unseen data, using only data from a single source domain. Current research focuses on developing methods that leverage data augmentation techniques (including adversarial and style-based approaches), disentangle domain-invariant features from domain-specific ones, and employ contrastive learning or meta-learning strategies to improve model robustness. The significance of SDG lies in its potential to address data scarcity and privacy concerns in various applications, particularly in medical imaging and other fields where acquiring diverse datasets is difficult or ethically problematic.