Decom--CAM：告诉我你看到了什么，详细地！通过分解类激活图进行特征级解释
Decom--CAM: Tell Me What You See, In Details! Feature-Level Interpretation via Decomposition Class Activation Map
AI总结：
本文提出Decom-CAM，通过奇异值分解将激活图分解为正交特征，生成更精确的显著性图，并引入基于分类准确率的评估协议，显著优于现有方法，推动深度模型特征级解释。
摘要：
深度学习的解释仍然是一个非常具有挑战性的问题。尽管类激活图（CAM）通过突出对象位置被广泛用于解释深度模型的预测，但它未能提供模型用于决策的显著特征的洞察。此外，现有的评估协议往往忽视了可解释性性能与模型决策质量之间的相关性，这提出了一个更根本的问题。本文提出了一种新的两阶段可解释性方法，称为分解类激活图（Decom-CAM），它提供了模型预测的特征级解释。Decom-CAM使用奇异值分解将中间激活图分解为正交特征，并通过整合它们生成显著性图。特征的正交性使CAM能够捕获局部特征，并可用于定位输入图像中的眼睛、鼻子和面部等语义组件，使其对深度模型解释更有益。为了确保全面比较，我们引入了一种新的评估协议，根据分类准确率结果将数据集划分为子集，并分别评估每个子集上的可解释性性能。我们的实验表明，所提出的Decom-CAM通过在所有分类准确率水平上生成更精确的显著性图，显著优于当前最先进的方法。结合我们的特征级可解释性方法，本文可能为理解深度神经网络的决策过程开辟新的方向。
英文摘要：
Interpretation of deep learning remains a very challenging problem. Although the Class Activation Map (CAM) is widely used to interpret deep model predictions by highlighting object location, it fails to provide insight into the salient features used by the model to make decisions. Furthermore, existing evaluation protocols often overlook the correlation between interpretability performance and the model's decision quality, which presents a more fundamental issue. This paper proposes a new two-stage interpretability method called the Decomposition Class Activation Map (Decom-CAM), which offers a feature-level interpretation of the model's prediction. Decom-CAM decomposes intermediate activation maps into orthogonal features using singular value decomposition and generates saliency maps by integrating them. The orthogonality of features enables CAM to capture local features and can be used to pinpoint semantic components such as eyes, noses, and faces in the input image, making it more beneficial for deep model interpretation. To ensure a comprehensive comparison, we introduce a new evaluation protocol by dividing the dataset into subsets based on classification accuracy results and evaluating the interpretability performance on each subset separately. Our experiments demonstrate that the proposed Decom-CAM outperforms current state-of-the-art methods significantly by generating more precise saliency maps across all levels of classification accuracy. Combined with our feature-level interpretability approach, this paper could pave the way for a new direction for understanding the decision-making process of deep neural networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
