Mind the Gap: Bridging Prior Shift in Realistic Few-Shot Crop-Type Classification
Dirichlet Prior Augmentation (DirPA) simulates unknown label distribution shifts during training for few-shot crop classification. It uses Dirichlet distributions to proactively sample various class distributions, forcing the model to learn robust representations that generalize better when faced with real-world data imbalances.