Out of distribution robustness with pre-trained Bayesian neural networks. (arXiv:2206.12361v1 [cs.LG])

We develop ShiftMatch, a new training-data-dependent likelihood for out of
distribution (OOD) robustness in Bayesian neural networks (BNNs). ShiftMatch is
inspired by the training-data-dependent "EmpCov" priors from Izmailov et al.
(2021a) and efficiently matches test-time spatial correlations to those at
training time. Critically, ShiftMatch is designed to leave neural network
training unchanged, allowing it to use publically available samples from
pretrained BNNs. Using pre-trained HMC samples, ShiftMatch gives strong
performance improvements on CIFAR-10-C, outperforms EmpCov priors, and is
perhaps the …

arxiv bayesian distribution lg networks neural networks robustness