Bayesian Scattering: A Principled Baseline for Uncertainty on Image Data
Uncertainty quantification for image data is dominated by complex deep learning methods, yet the field lacks an interpretable, mathematically grounded baseline. We propose Bayesian scattering to fill this gap, serving as a first-step baseline akin to the role of Bayesian linear regression for tabular data. Our method couples the wavelet scattering transform-a deep, non-learned feature extractor-with a simple probabilistic head. Because scattering features are derived from geometric principles rather than learned, they avoid overfitting the training distribution. This helps provide sensible uncertainty estimates even under significant distribution shifts. We validate this on diverse tasks,...
| Authors | Bernardo Fichera, Zarko Ivkovic, Kjell Jorner et al. |
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| Primary category | cs.LG |
| Published | 21 March 2026 |
| Last updated at source | 21 March 2026 |
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