Cross Domain Generalization

Cross-domain generalization (CDG) focuses on developing machine learning models that maintain performance when applied to data from different distributions than those seen during training. Current research emphasizes improving CDG through advanced pre-trained architectures like Vision Transformers and techniques such as data augmentation, pseudo-labeling, and feature disentanglement, often within the context of specific tasks like image classification, text spotting, and medical image segmentation. The ability to build robust and adaptable models with strong CDG capabilities is crucial for deploying AI systems in real-world scenarios where data variability is inevitable, impacting fields ranging from healthcare to autonomous driving.