Test-time augmentation improves efficiency in conformal prediction

A new method using test-time augmentation (TTA) enhances conformal classifiers by reducing their prediction set sizes by 10-14% without retraining models. The approach is flexible, efficient, and effective across multiple datasets, models, and conformal scoring methods, especially under distribution shifts.