[2603.20908] Bayesian Scattering: A Principled Baseline for Uncertainty on Image Data

Computer Science > Machine Learning arXiv:2603.20908 (cs) [Submitted on 21 Mar 2026] Title:Bayesian Scattering: A Principled Baseline for Uncertainty on Image Data Authors:Bernardo Fichera, Zarko Ivkovic, Kjell Jorner, Philipp Hennig, Viacheslav Borovitskiy View a PDF of the paper titled Bayesian Scattering: A Principled Baseline for Uncertainty on Image Data, by Bernardo Fichera and 4 other authors View PDF Abstract: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, including medical imaging under institution shift, wealth mapping under country-to-country shift, and Bayesian optimization of molecular properties. Our results suggest that Bayesian scattering is a solid baseline for complex uncertainty quantification methods. Subjects: Machine Learning (cs.LG); Machine Learning (stat.ML) Cite as: arXiv:2603.20908 [cs.LG]   (or arXiv:2603.20...