We consider the problem of uncertainty estimation in the context of
(non-Bayesian) deep neural classification. In this context, all known methods
are based on extracting uncertainty signals from a trained network optimized to
solve the classification problem at hand. We demonstrate that such techniques
tend to introduce biased estimates for instances whose predictions are supposed
to be highly confident. We argue that this deficiency is an artifact of the
dynamics of training with SGD-like optimizers, and it has some properties
similar to overfitting. Based on this observation, we develop an uncertainty
estimation algorithm that selectively estimates the uncertainty of highly
confident points, using earlier snapshots of the trained model, before their
estimates are jittered (and way before they are ready for actual
classification). We present extensive experiments indicating that the proposed
algorithm provides uncertainty estimates that are consistently better than all
known methods.

本研究提出了一种基于模型历史快照的算法，用于在非贝叶斯深度神经分类中，有选择地估计高度自信点的不确定性，这解决了从已训练网络中提取不确定信号的已知方法所带来的偏差估计问题，研究表明所提出的算法比所有已知方法的不确定性估计结果更加准确。