Single Domain

Single-domain generalization (SDG) tackles the challenge of training machine learning models, particularly in image segmentation and object detection, that can generalize to unseen data when only a single source domain is available for training. Current research focuses on developing techniques to disentangle domain-specific and task-relevant features, often employing data augmentation strategies (e.g., frequency filtering, geometric transformations) and novel loss functions to improve model robustness. These advancements are crucial for addressing data scarcity issues in fields like medical imaging and improving the reliability of AI systems in real-world applications where access to diverse training data is limited.