提出新评估方法与高分辨率生成技术，提升卷积网络解释图的可靠性。
How to Evaluate and Refine your CAM
- 构建含真实归因的合成数据集，用于严格评估解释图质量
- 提出ARCC综合指标，更准确识别可信解释
- 设计RefineCAM融合多层特征，生成高分辨率解释图
类别归属图（CAM）为卷积神经网络决策提供局部解释。尽管广泛应用，由于缺乏真实解释作为基准，现有评估方法难以验证其有效性。同时，主流CAM方法生成的归因图分辨率较低，限制了细致可解释性。为此，本文构建一个包含真实归因的合成数据集，实现对现有评估指标的严谨比较。基于该数据集，我们分析现有指标并提出新的复合指标ARCC，能更可靠地识别出忠实的解释。针对分辨率低的问题，提出RefineCAM方法，通过聚合网络多层输出的CAM生成高分辨率归因图。实验结果表明，RefineCAM在所提评估标准下持续优于现有方法。
原文摘要 · Abstract (English)
Class attribution maps (CAMs) provide local explanations for the decisions of convolutional neural networks. While widely used in practice, the evaluation of CAMs remains challenging due to the lack of ground-truth explanations, making it difficult to evaluate the soundness of existing metrics. Independently, most commonly used CAM methods produce low-resolution attribution maps, which limits their usefulness for detailed interpretability. To address the evaluation challenge, we introduce a synthetic dataset with ground-truth attributions that enables a rigorous comparison of CAM evaluation metrics. Using this dataset, we analyze existing metrics and propose ARCC, a new composite metric that more reliably identifies faithful explanations. To address the low resolution issue, we introduce RefineCAM, a method that produces high-resolution attribution maps by aggregating CAMs across multiple network layers. Our results show that RefineCAM consistently outperforms existing methods according to the proposed evaluation.
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