We develop ShiftMatch, a new training-data-dependent likelihood for robustness to corruption 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 the neural network's training time likelihood unchanged, allowing it to use publicly available samples from pre-trained BNNs. Using pre-trained HMC samples, ShiftMatch gives strong performance improvements on CIFAR-10-C, outperforms EmpCov priors (though ShiftMatch uses extra information from a minibatch of corrupted test points), and is perhaps the first Bayesian method capable of convincingly outperforming plain deep ensembles.

翻译：我们开发了 " ShiftMatch " ( ShiftMatch ), 这是在巴伊西亚神经网络中加强腐败的新的培训数据依赖的可能性。 " ShiftMatch " 受Izmailov等人(2021a)的培训数据依赖“EmpCov”前科的启发,并且有效地将测试时空间相关性与培训时的测试时空间相关性相匹配。 关键地说, " ShiftMatch " 的设计使神经网络的培训时间概率保持不变,使其能够使用预先培训的BNNs的公开样本。 ShiftMatch使用预先培训的HMC样本,大大改进了CIFAR-10-C的性能,优于EmpCov的前科(尽管 ShiftMatch使用了来自一大批腐败测试点的额外信息 ), 也许是第一个能够令人信服地超越普通的深度昆虫的巴伊斯方法。