Bootstrap Your Own Variance
Polina Turishcheva Jason Ramapuram Sinead Williamson Dan Busbridge Eeshan Dhekane Russ Webb
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).