论文标题
预先训练的贝叶斯神经网络中腐败的鲁棒性
Robustness to corruption in pre-trained Bayesian neural networks
论文作者
论文摘要
我们开发了ShiftMatch，这是贝叶斯神经网络（BNNS）腐败的新型培训数据依赖性的可能性。 ShiftMatch的灵感来自Izmailov等人的培训data依赖性“ EmpCov”先验。 （2021a），并有效地匹配了在训练时与测试时间的空间相关性。至关重要的是，ShiftMatch旨在使神经网络的训练时间的可能性保持不变，从而使其可以使用预先训练的BNN的公开样品。使用预训练的HMC样品，ShiftMatch在CIFAR-10-C上进行了强大的性能改进，胜过EmpCov先验（尽管ShiftMatch使用了来自损坏的测试点的额外信息），并且也许是能够令人信服的贝叶斯方法，能够令人信服地胜过胜过纯粹的深度合奏。
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.
