The paper “Bootstrap Your Own Variance: Understanding Model Uncertainty with SSL and Bayesian Methods” was accepted at the Self-Supervised Learning workshop at NeurIPS 2023. It proposes BYOV, combining BYOL SSL algorithm with BBB Bayesian method to estimate model posteriors, showing that BYOV’s predictive standard deviation aligns well with a Gaussian distribution.
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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…
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