揭示CNN决策时神经元注意力演化动态，可视化其内部工作过程。
Neuron Abandoning Attention Flow: Visual Explanation of Dynamics inside CNN Models
- 设计级联神经元舍弃反向传播算法，追踪参与决策的神经元路径。
- 在9个CNN模型上验证，可清晰展示注意力流动与关键神经元贡献。
- 适合关注模型可解释性、图像分类与少样本学习的研究者。
本文提出一种神经元舍弃注意力流（NAFlow）方法，以解决视觉解释卷积神经网络（CNN）在做出分类决策时内部注意力演化动态的开放问题。设计了一种新颖的级联神经元舍弃反向传播算法，通过追踪所有层中参与预测的神经元来缓解被舍弃神经元带来的显著干扰。首先，提出神经元舍弃反向传播（NA-BP）模块，利用CNN中间层的逆函数生成反向传播特征图（BPFM），并舍弃不参与决策的神经元；级联的NA-BP模块计算重要性系数张量，并线性组合至BPFM以形成NAFlow。其次，为支持基于相似性度量的CNN模型可视化，引入新的通道贡献权重模块，通过雅可比矩阵计算重要性系数。所提NAFlow方法在九种广泛使用的CNN模型上进行了验证，涵盖通用图像分类、对比学习分类、少样本图像分类和图像检索等多种任务。
原文摘要 · Abstract (English)
In this paper, we present a Neuron Abandoning Attention Flow (NAFlow) method to address the open problem of visually explaining the attention evolution dynamics inside CNNs when making their classification decisions. A novel cascading neuron abandoning back-propagation algorithm is designed to trace neurons in all layers of a CNN that involve in making its prediction to address the problem of significant interference from abandoned neurons. Firstly, a Neuron Abandoning Back-Propagation (NA-BP) module is proposed to generate Back-Propagated Feature Maps (BPFM) by using the inverse function of the intermediate layers of CNN models, on which the neurons not used for decision-making are abandoned. Meanwhile, the cascading NA-BP modules calculate the tensors of importance coefficients which are linearly combined with the tensors of BPFMs to form the NAFlow. Secondly, to be able to visualize attention flow for similarity metric-based CNN models, a new channel contribution weights module is proposed to calculate the importance coefficients via Jacobian Matrix. The effectiveness of the proposed NAFlow is validated on nine widely-used CNN models for various tasks of general image classification, contrastive learning classification, few-shot image classification, and image retrieval.
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