Neural network quantization is an essential technique for deploying models on
resource-constrained devices. However, its impact on model perceptual fields,
particularly regarding class activation maps (CAMs), remains a significant area
of investigation. In this study, we explore how quantization alters the spatial
recognition ability of the perceptual field of vision models, shedding light on
the alignment between CAMs and visual saliency maps across various
architectures. Leveraging a dataset of 10,000 images from ImageNet, we
rigorously evaluate six diverse foundational CNNs: VGG16, ResNet50,
EfficientNet, MobileNet, SqueezeNet, and DenseNet. We uncover nuanced changes
in CAMs and their alignment with human visual saliency maps through systematic
quantization techniques applied to these models. Our findings reveal the
varying sensitivities of different architectures to quantization and underscore
its implications for real-world applications in terms of model performance and
interpretability. The primary contribution of this work revolves around
deepening our understanding of neural network quantization, providing insights
crucial for deploying efficient and interpretable models in practical settings.

本研究探讨了量化对视觉模型的感知领域特征，尤其是类别激活图（CAMs）的影响，并通过对六种不同 CNN 模型进行系统的量化技术评估，揭示了 CAMs 与人类视觉显著性图之间的对齐问题和不同模型对量化的敏感性，为实际应用中的模型性能和可解释性提供重要见解。