# Masked Gamma-SSL: Learning Uncertainty Estimation via Masked Image Modeling Source: [Masked Gamma-SSL: Learning Uncertainty Estimation via Masked Image Modeling]() Collection index: [llms.txt]() Data updated: 2025-10-26T02:23:12.204Z Authors: David S. W. Williams, Matthew Gadd, Paul Newman, Daniele De Martini Published: 2024-02-27T15:49:54.000Z ## Abstract This work proposes a semantic segmentation network that produces high-quality uncertainty estimates in a single forward pass. We exploit general representations from foundation models and unlabelled datasets through a Masked Image Modeling (MIM) approach, which is robust to augmentation hyper-parameters and simpler than previous techniques. For neural networks used in safety-critical applications, bias in the training data can lead to errors; therefore it is crucial to understand a network's limitations at run time and act accordingly. To this end, we test our proposed method on a number of test domains including the SAX Segmentation benchmark, which includes labelled test data from dense urban, rural and off-road driving domains. The proposed method consistently outperforms uncertainty estimation and Out-of-Distribution (OoD) techniques on this difficult benchmark. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()