发表机构
BRAC University; Bangladesh University of Engineering and Technology (BUET)（BRAC大学; 孟加拉国工程技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究训练后量化（PTQ）对五种CNN架构可解释性的影响，采用结合Grad - CAM和LIME的双重框架，在二分类数据集上比较全精度和量化模型，发现分类精度非可解释性稳定性可靠指标，架构选择与量化策略同样重要。
AI 中文摘要
训练后量化（PTQ）已成为在资源受限边缘设备上部署深度学习模型的实用解决方案，可将高精度浮点权重压缩为低精度表示而无需重新训练。以往研究表明量化很大程度上保留分类精度，但它是否保留模型内部推理仍未知。本研究系统评估了静态PTQ在INT8和INT4精度下对五种常用CNN架构（VGG19、ResNet18、EfficientNet - B0、DenseNet161和MobileNetV2）可解释性的影响。采用结合Grad - CAM进行空间注意力分析和LIME进行输入级特征归因的双重可解释性框架，在两个二分类数据集上系统比较全精度和量化模型。使用皮尔逊相关系数、结构相似性指数和前20% IoU等三个互补指标评估可解释性，并辅以删除/插入忠实性分析。结果表明分类精度不是低精度下可解释性稳定性的可靠指标。DenseNet161在两个精度水平上保持强大特征一致性，而EfficientNet - B0在INT8精度下虽有有竞争力的空间注意力和分类精度，但在输入级特征归因方面大幅下降。这些发现对高可解释性要求应用中量化模型的可靠部署有直接影响，表明架构选择与量化策略同样重要。
英文摘要
Post-training quantization (PTQ) has become a practical solution for deploying deep learning models on resource-constrained edge devices by compressing high-precision floating-point weights into low-precision representations without requiring retraining. Past research has demonstrated that quantization largely preserves classification accuracy; however, whether it also preserves the model's internal reasoning remains an open question. This study presents a systematic evaluation on how static PTQ affects the interpretability / explainability of five widely used CNN architectures: VGG19, ResNet18, EfficientNet-B0, DenseNet161, and MobileNetV2 at INT8 and INT4 precision. We employ a dual interpretability framework that combines Grad-CAM for spatial attention analysis with LIME for input-level feature attribution, and systematically compare full-precision and quantized models on two binary classification datasets. Interpretability is evaluated using three complementary metrics: the Pearson correlation coefficient, structural similarity index, and top-20% IoU to capture distributional and structural variations in model explanations, supplemented by deletion/insertion faithfulness analysis. The results show that classification accuracy is not a reliable indicator of interpretability stability under reduced precision. DenseNet161 maintains strong feature consistency across both precision levels, whereas EfficientNet-B0, despite achieving competitive spatial attention and classification accuracy at INT8 precision, exhibits a substantial degradation in input-level feature attribution. These findings have direct implications for the trustworthy deployment of quantized models in applications with high interpretability requirements, demonstrating that architecture selection is as important as the quantization strategy.
Comments12 pages, 3 Figures
