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。

量化对神经网络感知的影响：量化如何改变视觉模型的感知领域？

Quantization Effects on Neural Networks Perception: How would  quantization change the perceptual field of vision models?

Despite the significant progress in face recognition in the past years, they
are often treated as "black boxes" and have been criticized for lacking
explainability. It becomes increasingly important to understand the
characteristics and decisions of deep face recognition systems to make them
more acceptable to the public. Explainable face recognition (XFR) refers to the
problem of interpreting why the recognition model matches a probe face with one
identity over others. Recent studies have explored use of visual saliency maps
as an explanation, but they often lack a deeper analysis in the context of face
recognition. This paper starts by proposing a rigorous definition of
explainable face recognition (XFR) which focuses on the decision-making process
of the deep recognition model. Following the new definition, a similarity-based
RISE algorithm (S-RISE) is then introduced to produce high-quality visual
saliency maps. Furthermore, an evaluation approach is proposed to
systematically validate the reliability and accuracy of general visual
saliency-based XFR methods.

本文研究了人脸识别技术中存在的 “黑匣子” 问题，提出了可解释人脸识别的定义，并引入了基于相似性的 RISE 算法来产生高质量的视觉显著图，进一步提出了评估方法来系统地验证可靠性和准确性。