Incorporating Unlabelled Data into Bayesian Neural Networks

Mrinank Sharma; Tom Rainforth; Yee Whye Teh; Vincent Fortuin

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arXiv · 2304.01762

Incorporating Unlabelled Data into Bayesian Neural Networks

Mrinank Sharma·Tom Rainforth·Yee Whye Teh·Vincent Fortuin

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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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Mrinank Sharma, Tom Rainforth, Yee Whye Teh, Vincent Fortuin. 2023-04-04. Incorporating Unlabelled Data into Bayesian Neural Networks. https://arxiv.org/abs/2304.01762

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