用Grad-CAM揭示深度学习如何从轨迹中识别异常扩散机制
Exploring how deep learning decodes anomalous diffusion via Grad-CAM
- 用Grad-CAM分析ResNet模型关注的轨迹关键区域
- 发现高层网络捕捉大尺度特征，低层捕捉小尺度特征
- 结果可提升模型抗噪声能力，适合研究者解释AI决策
尽管深度学习已成功用于数据驱动的异常扩散机制分类，但其工作原理仍不明确。本研究采用梯度加权类激活图（Grad-CAM）技术，探究基于ResNet的深度学习模型如何从原始轨迹数据中识别特定异常扩散模型的独特特征。结果表明，Grad-CAM能有效揭示轨迹中蕴含扩散机制关键信息的区域，可用于增强分类器对测量噪声的鲁棒性。此外，深度学习在不同时空尺度上提取了各扩散机制的独特统计特征：高层网络关注大尺度特征，低层网络则聚焦小尺度特征。
原文摘要 · Abstract (English)
While deep learning has been successfully applied to the data-driven classification of anomalous diffusion mechanisms, how the algorithm achieves the feat still remains a mystery. In this study, we use a well-known technique aimed at achieving explainable AI, namely the Gradient-weighted Class Activation Map (Grad-CAM), to investigate how deep learning (implemented by ResNets) recognizes the distinctive features of a particular anomalous diffusion model from the raw trajectory data. Our results show that Grad-CAM reveals the portions of the trajectory that hold crucial information about the underlying mechanism of anomalous diffusion, which can be utilized to enhance the robustness of the trained classifier against the measurement noise. Moreover, we observe that deep learning distills unique statistical characteristics of different diffusion mechanisms at various spatiotemporal scales, with larger-scale (smaller-scale) features identified at higher (lower) layers.
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