Distribution Accuracy

Distribution accuracy in machine learning focuses on how well models generalize to data differing from their training distribution (out-of-distribution or OOD). Current research investigates methods to predict OOD performance from in-distribution (ID) metrics, analyzes the relationship between ID and OOD accuracy (sometimes finding unexpected negative correlations), and explores techniques like metric learning, data augmentation, and model adaptation (e.g., linear probing) to improve OOD robustness. Understanding and improving distribution accuracy is crucial for building reliable and safe AI systems, particularly in applications where encountering unseen data is common.