  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.
