Prior Distribution and Model Confidence
This work investigates how training data distribution affects the generalization of image classification models and proposes a model-agnostic, retraining-free confidence assessment framework. The method fuses multiple embeddings into a unified embedding space to jointly characterize the structure of the training distribution; it then employs an adaptive distance metric to quantify each sample’s deviation from this distribution, enabling confidence calibration, low-confidence prediction filtering, and out-of-distribution (OOD) detection. The framework is architecture-agnostic and domain-transferable, delivering consistent improvements across diverse backbones—including ResNet and Vision Transformers—without architectural modification or fine-tuning. Notably, accuracy gains are especially pronounced after filtering low-confidence predictions. Empirically, it enhances classification robustness and reliability under distribution shifts, offering a lightweight, plug-and-play, distribution-aware inference mechanism for trustworthy AI systems.