Exploring how deep learning decodes anomalous diffusion via Grad-CAM

• URL: http://arxiv.org/abs/2410.16345v1
• Date: Mon, 21 Oct 2024 13:17:49 GMT
• Title: Exploring how deep learning decodes anomalous diffusion via Grad-CAM
• Authors: Jaeyong Bae, Yongjoo Baek, Hawoong Jeong,
• Abstract summary: In this study, we use a well-implemented technique aimed at achieving explainable AI, namely the Gradient-Class Activation Map (Grad-CAM) Our results show that Grad-CAM reveals the portions of the trajectory that hold crucial information about the underlying mechanism of anomalous diffusion.
• Score: 2.048226951354646
• License:
• Abstract: 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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