ReProCP: Refined Prototypical Conformal Prediction

Conformal prediction has become a popular framework for distribution-free uncertainty quantification, but its effectiveness in low-data regimes is limited by the data splitting required for model fitting and calibration. Full conformal prediction avoids this split by reusing all labeled examples but typically incurs the prohibitive cost of repeatedly refitting the model. We introduce ReProCP (Refined Prototypical Conformal Prediction), an efficient full conformal method built on pretrained self-supervised vision models. Our key insight is that self-supervised embeddings provide not only a strong foundation for accurate transfer learning, but also an effective representation for uncertainty quantification. Operating directly in this embedding space, ReProCP employs a training-free, prototype-based nonconformity score that can be efficiently evaluated across candidate labels, eliminating repeated model fitting. ReProCP further leverages unlabeled images to refine the embedding geometry through dimensionality reduction, whitening, and neighborhood smoothing, yielding more compact prediction sets without additional annotations. Across diverse image-classification datasets, self-supervised backbones, and labeling budgets, ReProCP consistently produces smaller prediction sets than competing conformal baselines, with increasingly pronounced gains as the labeling budget decreases.