Although deep neural networks have made significant progress in confidence calibration, conventional methods like temperature scaling and histogram binning face notable limitations in dynamic open-world scenarios due to their reliance on global static parameters or idealized data distribution assumptions. To address these challenges, we propose a Dynamic Confidence Propagation and Alternating Normalization (DCP-AN) framework. Our approach introduces three key innovations: (1) a bidirectional alternating propagation mechanism that enables sample-class confidence synergy through entropy-driven horizontal normalization and KL-divergence-weighted vertical normalization; (2) an adaptive temperature field with dynamic coefficients that achieves differential calibration for non-uniform confidence biases; and (3) a theoretically guaranteed spectral convergence process within 15 iterations. Extensive experiments demonstrate that DCP-AN achieves remarkable improvements: on ImageNet-LT, it boosts tail-class accuracy by 10.3% and reduces expected calibration error by 56%; in cross-domain adaptation, it decreases domain discrepancy by 24% while improving target domain accuracy by 5.5%. Furthermore, DCP-AN maintains computational efficiency with a GPU latency of 1.05 ms and memory overhead under 0.5 MB, making it suitable for real-time deployment.
He et al. (Tue,) studied this question.