A Fourier perspective on the learning dynamics of neural networks: from sample complexities to mechanistic insights
Neural networks trained with gradient-based methods exhibit a strong simplicity bias: they learn simpler statistical features of their data before moving to more complex features. Previous analyses of this phenomenon have largely focused on settings with (quasi-)isotropic inputs. In this work, we study the simplicity bias from a Fourier perspective, which allows us to include two key features of natural images in the analysis: approximate translation-invariance and power-law spectra. We first show experimentally that simple neural networks trained on image classification tasks first rely on amplitude information -- related to pair-wise correlations between pixels -- before exploiting phas...
| Authors | Fabiola Ricci, Claudia Merger, Sebastian Goldt |
|---|---|
| Primary category | stat.ML |
| Published | 16 May 2026 |
| Last updated at source | 16 May 2026 |
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