research areaComputer Vision, research areaMethods and AlgorithmsWorkshop at NeurIPS
content type paperpublished December 2023

Bootstrap Your Own Variance

In collaboration with University of Göttingen

AuthorsPolina Turishcheva*, Jason Ramapuram*, Sinead Williamson*, Dan Busbridge, Eeshan Dhekane, Russ Webb

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This paper was accepted at the workshop Self-Supervised Learning - Theory and Practice at NeurIPS 2023.

*=Equal Contributors

Understanding model uncertainty is important for many applications. We propose Bootstrap Your Own Variance (BYOV), combining Bootstrap Your Own Latent (BYOL), a negative-free Self-Supervised Learning (SSL) algorithm, with Bayes by Backprop (BBB), a Bayesian method for estimating model posteriors. We find that the learned predictive std of BYOV vs. a supervised BBB model is well captured by a Gaussian distribution, providing preliminary evidence that the learned parameter posterior is useful for label free uncertainty estimation. BYOV improves upon the deterministic BYOL baseline (+2.83% test ECE, +1.03% test Brier) and presents better calibration and reliability when tested with various augmentations (eg: +2.4% test ECE, +1.2% test Brier for Salt & Pepper noise).

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