Incorporating Unlabelled Data into Bayesian Neural Networks

• URL: http://arxiv.org/abs/2304.01762v3
• Date: Fri, 30 Aug 2024 12:51:53 GMT
• Title: Incorporating Unlabelled Data into Bayesian Neural Networks
• Authors: Mrinank Sharma, Tom Rainforth, Yee Whye Teh, Vincent Fortuin,
• Abstract summary: We introduce Self-Supervised Bayesian Neural Networks, which use unlabelled data to learn models with suitable prior predictive distributions. We show that the prior predictive distributions of self-supervised BNNs capture problem semantics better than conventional BNN priors. Our approach offers improved predictive performance over conventional BNNs, especially in low-budget regimes.
• Score: 48.25555899636015
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Conventional Bayesian Neural Networks (BNNs) are unable to leverage unlabelled data to improve their predictions. To overcome this limitation, we introduce Self-Supervised Bayesian Neural Networks, which use unlabelled data to learn models with suitable prior predictive distributions. This is achieved by leveraging contrastive pretraining techniques and optimising a variational lower bound. We then show that the prior predictive distributions of self-supervised BNNs capture problem semantics better than conventional BNN priors. In turn, our approach offers improved predictive performance over conventional BNNs, especially in low-budget regimes.

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