Recent advances in deep learning have achieved impressive gains in
classification accuracy on a variety of types of data, including images and
text. Despite these gains, however, concerns have been raised about the
calibration, robustness, and interpretability of these models. In this paper we
propose a simple way to modify any conventional deep architecture to
automatically provide more transparent explanations for classification
decisions, as well as an intuitive notion of the credibility of each
prediction. Specifically, we draw on ideas from nonparametric kernel
regression, and propose to predict labels based on a weighted sum of training
instances, where the weights are determined by distance in a learned
instance-embedding space. Working within the framework of conformal methods, we
propose a new measure of nonconformity suggested by our model, and
experimentally validate the accompanying theoretical expectations,
demonstrating improved transparency, controlled error rates, and robustness to
out-of-domain data, without compromising on accuracy or calibration.

本文提出了一种简单的方法，通过从非参数核回归中的一些想法得出训练实例的加权总和来预测标签，并在符合性方法的框架内提出了一种新的非一致性测量法，实验验证了理论预期，证明了在不影响准确性或校准的情况下，提高了透明度、可控的错误率和抵抗域外数据的鲁棒性。