提出新方法提升神经网络可解释性，更好定位关键特征。
Advancing Attribution-Based Neural Network Explainability through Relative Absolute Magnitude Layer-Wise Relevance Propagation and Multi-Component Evaluation
- 基于相对绝对值改进层间重要性传播，更精准分配输入重要性
- 在ImageNet和PascalVOC上优于现有方法，显著提升解释质量
- 提出融合忠实性、鲁棒性和对比性的新评估指标，适合模型对比
深度神经网络性能的提升推动了多个领域的技术革新，但其黑箱特性限制了在需要模型可解释性的场景中的应用。近年来，层间重要性传播（LRP）成为主流可解释性方法，通过像素级分解非线性分类器来分配局部重要性。随着归因方法研究的发展，评估其性能的需求日益迫切。尽管已有多种度量标准，分别衡量忠实性、鲁棒性和定位能力，但尚无单一指标适用于所有场景，研究者常需综合多指标评估。本文针对现有LRP方法的不足，提出一种基于相对绝对值的新型层间重要性传播方法，并将其应用于最近提出的Vision Transformer架构。在ImageNet和PascalVOC两个图像分类数据集上的实验表明，所提方法显著优于现有方法。此外，本文分析了当前评估指标的局限性，提出一个融合忠实性、鲁棒性和对比性的新评价指标，并用于评估多种归因方法的性能。代码已开源：https://github.com/davor10105/relative-absolute-magnitude-propagation。
原文摘要 · Abstract (English)
Recent advancement in deep-neural network performance led to the development of new state-of-the-art approaches in numerous areas. However, the black-box nature of neural networks often prohibits their use in areas where model explainability and model transparency are crucial. Over the years, researchers proposed many algorithms to aid neural network understanding and provide additional information to the human expert. One of the most popular methods being Layer-Wise Relevance Propagation (LRP). This method assigns local relevance based on the pixel-wise decomposition of nonlinear classifiers. With the rise of attribution method research, there has emerged a pressing need to assess and evaluate their performance. Numerous metrics have been proposed, each assessing an individual property of attribution methods such as faithfulness, robustness or localization. Unfortunately, no single metric is deemed optimal for every case, and researchers often use several metrics to test the quality of the attribution maps. In this work, we address the shortcomings of the current LRP formulations and introduce a novel method for determining the relevance of input neurons through layer-wise relevance propagation. Furthermore, we apply this approach to the recently developed Vision Transformer architecture and evaluate its performance against existing methods on two image classification datasets, namely ImageNet and PascalVOC. Our results clearly demonstrate the advantage of our proposed method. Furthermore, we discuss the insufficiencies of current evaluation metrics for attribution-based explainability and propose a new evaluation metric that combines the notions of faithfulness, robustness and contrastiveness. We utilize this new metric to evaluate the performance of various attribution-based methods. Our code is available at: https://github.com/davor10105/relative-absolute-magnitude-propagation
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