用近邻证据动态调整判断标准，让模型更懂何时该怀疑自己。
Proximity-Based Evidence Retrieval for Uncertainty-Aware Neural Networks
- 基于嵌入空间找相似样本，用可信度融合做个性化判断
- 在CIFAR上错误判断减少40%以上，且少量证据就见效
- 适合需要可解释决策的医疗、金融等高风险场景
本文提出一种不确定性感知决策中的证据检索机制，将单一全局阈值替换为基于证据的实例自适应判据。对每个测试样本，在嵌入空间中检索其近邻样例，利用德克斯特-沙弗理论融合这些样例的预测分布，生成的融合信念作为个体化阈值。由于支持证据明确，决策过程透明可审计。在使用BiT和ViT主干的CIFAR-10/100数据集上实验表明，该方法在不确定性感知性能上达到或优于熵阈值法，且明显减少错误置信预测，同时保持可持续的人工审查负载。值得注意的是，仅需少数证据即可实现性能提升，增加证据数量带来的改善有限。结果表明，基于证据的标记比固定熵阈值更具可靠性与可解释性。
原文摘要 · Abstract (English)
This work proposes an evidence-retrieval mechanism for uncertainty-aware decision-making that replaces a single global cutoff with an evidence-conditioned, instance-adaptive criterion. For each test instance, proximal exemplars are retrieved in an embedding space; their predictive distributions are fused via Dempster-Shafer theory. The resulting fused belief acts as a per-instance thresholding mechanism. Because the supporting evidences are explicit, decisions are transparent and auditable. Experiments on CIFAR-10/100 with BiT and ViT backbones show higher or comparable uncertainty-aware performance with materially fewer confidently incorrect outcomes and a sustainable review load compared with applying threshold on prediction entropy. Notably, only a few evidences are sufficient to realize these gains; increasing the evidence set yields only modest changes. These results indicate that evidence-conditioned tagging provides a more reliable and interpretable alternative to fixed prediction entropy thresholds for operational uncertainty-aware decision-making.
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