Researchers have proposed various methods for visually interpreting the Convolutional Neural Network (CNN) via saliency maps, which include Class-Activation-Map (CAM) based approaches as a leading family. However, in terms of the internal design logic, existing CAM-based approaches often overlook the causal perspective that answers the core "why" question to help humans understand the explanation. Additionally, current CNN explanations lack the consideration of both necessity and sufficiency, two complementary sides of a desirable explanation. This paper presents a causality-driven framework, SUNY, designed to rationalize the explanations toward better human understanding. Using the CNN model's input features or internal filters as hypothetical causes, SUNY generates explanations by bi-directional quantifications on both the necessary and sufficient perspectives. Extensive evaluations justify that SUNY not only produces more informative and convincing explanations from the angles of necessity and sufficiency, but also achieves performances competitive to other approaches across different CNN architectures over large-scale datasets, including ILSVRC2012 and CUB-200-2011.

翻译：研究者已提出多种通过显著性图可视化解释卷积神经网络（CNN）的方法，其中基于类激活图（CAM）的方法构成主要研究分支。然而，就内在设计逻辑而言，现有CAM类方法常忽视因果视角，该视角能回答核心的"为什么"问题以帮助人类理解解释过程。此外，当前CNN解释缺乏对必要性与充分性——理想解释的两个互补维度——的同步考量。本文提出一个因果驱动框架SUNY，旨在通过理性化解释过程提升人类理解水平。该框架以CNN模型的输入特征或内部滤波器作为假设性原因，通过必要性维度和充分性维度的双向量化生成解释。大量实验证明，SUNY不仅从必要性与充分性角度生成更具信息量且更令人信服的解释，还在包含ILSVRC2012与CUB-200-2011的大规模数据集上，针对不同CNN架构取得了具有竞争力的性能表现。