让图像分类模型既指出概念位置，又量化其对预测的贡献。
Visual-TCAV: Concept-based Attribution and Saliency Maps for Post-hoc Explainability in Image Classification
- 用CAV生成不依赖类别的概念激活图，定位概念所在区域。
- 基于集成梯度扩展，计算特定概念对任一类别预测的贡献值。
- 在已知真实解释的实验中表现优于TCAV，适合需要可解释性的场景。
卷积神经网络（CNN）在图像分类任务中表现出色，但其预测过程难以解释。现有显著性方法可定位模型关注区域，但无法说明特定概念对预测的影响；而概念基础方法（如TCAV）虽能衡量模型对概念的敏感性，却无法提供具体预测中概念的贡献程度与空间位置。本文提出Visual-TCAV，一种新型可解释性框架，融合局部与全局解释能力。该方法利用概念激活向量（CAVs）生成与类别无关的概念显著性图，展示模型识别某概念的位置；同时，通过集成梯度的泛化形式，估算任意概念对特定类别输出的贡献值。我们在可控实验中评估其忠实性，结果表明其与真实解释的对齐度优于TCAV。代码已开源：https://github.com/DataSciencePolimi/Visual-TCAV。
原文摘要 · Abstract (English)
Convolutional Neural Networks (CNNs) have shown remarkable performance in image classification. However, interpreting their predictions is challenging due to the size and complexity of these models. State-of-the-art saliency methods generate local explanations highlighting the area in the input image where a class is identified but cannot explain how a concept of interest contributes to the prediction. On the other hand, concept-based methods, such as TCAV, provide insights into how sensitive the network is to a human-defined concept but cannot compute its attribution in a specific prediction nor show its location within the input image. We introduce Visual-TCAV, a novel explainability framework aiming to bridge the gap between these methods by providing both local and global explanations. Visual-TCAV uses Concept Activation Vectors (CAVs) to generate class-agnostic saliency maps that show where the network recognizes a certain concept. Moreover, it can estimate the attribution of these concepts to the output of any class using a generalization of Integrated Gradients. We evaluate the method's faithfulness via a controlled experiment where the ground truth for explanations is known, showing better ground truth alignment than TCAV. Our code is available at https://github.com/DataSciencePolimi/Visual-TCAV.
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