图像分类神经网络中高权重神经元是否重要？

中文摘要 AI 辅助

随着图像分类神经网络模型的发展，神经元在剪枝、后门防御和可解释性方面发挥着关键作用。然而，现有工作在权重与重要性的关系上并不明确。我们通过一种神经元重要性评估方法进行了三个实验来解决这个问题：量化高权重神经元与影响准确率的神经元之间的重叠，分析高权重神经元的扰动效应，以及测试高权重神经元消融后的再训练准确率。在CIFAR-10和Mini-ImageNet上的实验揭示了关键模式。重叠分析表明，前10%的高权重神经元与重要神经元的最大重叠率仅约为25%，在后续区间进一步下降。扰动测试发现，前10%的高权重神经元在某些操作下会导致45%-80%的准确率下降，而随机扰动的下降率为3%-7%，但其中三分之一的影响最小。消融-再训练结果表明，去除前10%的高权重神经元会使准确率比基线低10%-20%且无法恢复，而去除前0.1%则允许接近完全恢复。值得注意的是，一些低权重区间在受到扰动时会出现10%-17%的下降，与中等权重的高权重神经元相当。这些结果证实并非所有高权重神经元都重要：它们的重要性是非线性的。低权重神经元也有显著贡献。这挑战了权重与重要性的等价性，提供了关于神经元作用的更精确见解。它支持了如加密中优先考虑关键高权重神经元和剪枝中去除非关键神经元等应用，推动了神经网络分析。

英文摘要

As neural network models for image classification advance, neurons play critical roles in pruning, backdoor defense, and interpretability. Yet existing work lacks clarity on the weight-importance relationship. We address this with a neuron importance assessment method using three experiments: quantifying overlap between high-weight and accuracy-impacting neurons, analyzing high-weight neuron perturbation effects, and testing post-retraining accuracy after high-weight neuron ablation. Experiments on CIFAR-10 and Mini-ImageNet reveal key patterns. Overlap analysis shows top 10\% high-weight neurons overlap with important ones by only about 25\% at maximum, dropping further in subsequent intervals. Perturbation tests find top 10\% high-weight neurons cause 45-80\% accuracy degradation under certain operations compared to 3-7\% for random perturbations, but a third of them show minimal impact. Ablation-retraining results show removing top 10\% high-weight neurons leaves accuracy 10-20\% below baseline with no recovery, while ablating top 0.1\% allows near-full recovery. Notably, some low-weight intervals show 10-17\% degradation when perturbed, comparable to mid-range high-weight neurons. These results confirm not all high-weight neurons are important: their importance is nonlinear. Low-weight neurons also contribute significantly. This challenges weight-importance equivalence, offering refined neuron role insights. It supports applications like encryption prioritizing critical high-weight neurons and pruning removing non-critical ones, advancing neural network analysis.

发表机构

• Artificial Intelligence and Digital Economy Guangdong Provincial Laboratory（广东省人工智能与数字经济实验室）

机构由 AI 辅助整理，请以论文原文为准。