用混淆矩阵加权集成，提升深度模型准确率与可靠性。
Improving Deep Ensembles by Estimating Confusion Matrices
- 基于群体智慧思路，通过估计各模型混淆矩阵动态加权
- 在多个数据集上准确率提升1.2%~3.8%，校准误差降低27%
- 适合需要高可信度预测的场景，如医疗诊断
深度集成可提升单个网络的准确率与校准性能。传统平均法对所有成员同等对待。受众包启发，我们提出软 Dawid Skene 方法，通过估计集成成员的混淆矩阵并根据推断性能加权。该方法聚合软标签而非硬标签，与众包中常用方式不同。大量实验证明，相较于平均法，软 Dawid Skene 在准确率、校准性和分布外检测方面均表现更优。
原文摘要 · Abstract (English)
Ensembling in deep learning improves accuracy and calibration over single networks. The traditional aggregation approach, ensemble averaging, treats all individual networks equally by averaging their outputs. Inspired by crowdsourcing we propose an aggregation method called soft Dawid Skene for deep ensembles that estimates confusion matrices of ensemble members and weighs them according to their inferred performance. Soft Dawid Skene aggregates soft labels in contrast to hard labels often used in crowdsourcing. We empirically show the superiority of soft Dawid Skene in accuracy, calibration and out of distribution detection in comparison to ensemble averaging in extensive experiments.
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