The Neural Echo: A Signal Processing Perspective for Understanding Neural Networks
We introduce the neural echo as a tool for understanding the behavior of neural networks. It generalizes the model-based concepts of impulse responses, diffusion echoes, and filter echoes to learning-based methods. It provides local, space-adaptive impulse responses and filter kernels for a neural network, its so-called echoes. These echoes depend on the input image and can be visualized to understand the learned dynamics of the network via an affine mapping. Neural echoes build a bridge from classical signal processing to modern explainable AI. They are very general and can be applied to both image-to-image and classification networks, with convolutional or fully connected structure, of ...
| Authors | Chongbiao Wang, Daniel Gaa, Joachim Weickert et al. |
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| Primary category | eess.SP |
| Published | 5 August 2026 |
| Last updated at source | 5 August 2026 |
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