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Sharma, M.* ; Rainforth, T.* ; Teh, Y.W.* ; Fortuin, V.

Incorporating unlabelled data into bayesian neural networks.

Trans. Machine Learn. Res. 2024, accepted (2024)

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Abstract
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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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