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arXiv Paper Examines How Input Noise Variability Affects Neural Network Robustness

A new arXiv preprint argues that treating all input noise as equivalent may limit the robustness of neural networks. The work focuses on geophysical and seismic data, where heterogeneous noise from active field sites can hide weak events and hinder automated analysis. It suggests that robustness evaluations should account for differences in noise characteristics rather than assuming a uniform perturbation model.

robustness/noise/seismology/deep-learning