Retraining deep neural networks when new data arrives is typically
computationally expensive. Moreover, certain applications do not allow such
costly retraining due to time or computational constraints. Fast Bayesian
updates are a possible solution to this issue. Therefore, we propose a Bayesian
update based on Monte-Carlo samples and a last-layer Laplace approximation for
different Bayesian neural network types, i.e., Dropout, Ensemble, and Spectral
Normalized Neural Gaussian Process (SNGP). In a large-scale evaluation study,
we show that our updates combined with SNGP represent a fast and competitive
alternative to costly retraining. As a use case, we combine the Bayesian
updates for SNGP with different sequential query strategies to exemplarily
demonstrate their improved selection performance in active learning.

针对新数据到来时重训练深度神经网络通常具有计算成本高的问题，因此本文提出了基于 Monte-Carlo 采样和最后一层 Laplace 逼近的贝叶斯更新方法，用于不同类型的贝叶斯神经网络，即 Dropout，Ensemble 和 SNGP，大规模评估研究表明该更新结合 SNGP 是昂贵重新训练的快速而有竞争力的替代方案。同时，将其用于不同顺序查询策略的精选性能的改进，以演示其在主动学习中的应用。