[论文解读] Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs

Ruigang Fu, Qingyong Hu+4 more|arXiv (Cornell University)|Aug 5, 2020

Explainable Artificial Intelligence (XAI)参考文献 13被引用 151

一句话总结

XGrad-CAM 引入两个公理（敏感性和守恒性）来引导类判别型 CNN 可视化，提供在理论上比 Grad-CAM 更有依据的增强，同时保持高效并与任意 CNN 兼容。

ABSTRACT

To have a better understanding and usage of Convolution Neural Networks (CNNs), the visualization and interpretation of CNNs has attracted increasing attention in recent years. In particular, several Class Activation Mapping (CAM) methods have been proposed to discover the connection between CNN's decision and image regions. In spite of the reasonable visualization, lack of clear and sufficient theoretical support is the main limitation of these methods. In this paper, we introduce two axioms -- Conservation and Sensitivity -- to the visualization paradigm of the CAM methods. Meanwhile, a dedicated Axiom-based Grad-CAM (XGrad-CAM) is proposed to satisfy these axioms as much as possible. Experiments demonstrate that XGrad-CAM is an enhanced version of Grad-CAM in terms of conservation and sensitivity. It is able to achieve better visualization performance than Grad-CAM, while also be class-discriminative and easy-to-implement compared with Grad-CAM++ and Ablation-CAM. The code is available at https://github.com/Fu0511/XGrad-CAM.

研究动机与目标

• Motivate the interpretability gap in CNN visualizations and establish two axioms (Sensitivity and Conservation) to guide CAM methods.
• Derive a linear CAM formulation that satisfies these axioms as much as possible.
• Propose XGrad-CAM (and Guided XGrad-CAM) as theoretically grounded, class-discriminative visualization tools for arbitrary CNNs.

提出的方法

• Formulate CAM as a linear combination of target-layer feature maps with weights w_c^k.
• Define weights by solving an optimization objective that enforces Sensitivity and Conservation (Eq. 4).
• Derive an approximate closed-form solution alpha_c^k (Eq. 7) to obtain XGrad-CAM (Eq. 8).
• Prove that XGrad-CAM generalizes CAM and reduces to CAM for GAP-CNNs (Appendix).
• Optional: combine XGrad-CAM with Guided Backprop to obtain Guided XGrad-CAM for finer details.]
• research_questions:
• Does XGrad-CAM satisfy the proposed axioms of Sensitivity and Conservation more effectively than existing CAM methods?
• Is XGrad-CAM class-discriminative and efficient across common CNN architectures?
• How does XGrad-CAM perform in localization (perturbation) analyses compared to Grad-CAM, Grad-CAM++, and Ablation-CAM?
• What is the impact of the chosen target layer on visualization quality across architectures?

实验结果

研究问题

• RQ1 Does XGrad-CAM satisfy the proposed axioms of Sensitivity and Conservation more effectively than existing CAM methods?
• RQ2 Is XGrad-CAM class-discriminative and efficient across common CNN architectures?
• RQ3 How does XGrad-CAM perform in localization (perturbation) analyses compared to Grad-CAM, Grad-CAM++, and Ablation-CAM?
• RQ4 What is the impact of the chosen target layer on visualization quality across architectures?

主要发现

• XGrad-CAM achieves better adherence to the two axioms (Sensitivity and Conservation) than Grad-CAM and Grad-CAM++ on the ILSVRC-12 validation set.
• In class-discrimination tests, Grad-CAM, Ablation-CAM, and XGrad-CAM show similar performance, while Grad-CAM++ performs worse.
• In perturbation analyses, XGrad-CAM yields higher confidence drop than Grad-CAM, with Ablation-CAM being more time-consuming; Grad-CAM++ may have higher raw performance but lower class-discriminability.
• XGrad-CAM provides stronger localization and is more efficient than Ablation-CAM (which is about 40x slower).
• Guided XGrad-CAM offers additional detail by combining XGrad-CAM with Guided Backprop.