论文标题
PAC置信度预测深神经网络分类器
PAC Confidence Predictions for Deep Neural Network Classifiers
论文作者
论文摘要
在安全关键环境中部署深层神经网络（DNN）的关键挑战是需要提供严格的方法来量化其不确定性。在本文中，我们提出了一种新型算法，用于构建具有可证明的正确性保证的DNN的预测分类信心。我们的方法使用clopper-pearson的置信区间与直方图融合方法进行校准预测，用于二项式分布。此外，我们展示了如何使用我们的预测信心在两种情况下实现下游保证：（i）快速DNN推理，在这里我们演示如何以一种准确但不准确的DNN构成以准确但慢的DNN的形式，以一种严格的方式以一种确保确保dnn的安全性，以确保dnn的安全性，以提高性能，以提高绩效，并确保dn dnn的安全性。在我们的实验中，我们证明我们的方法可用于为最新的DNN提供保证。
A key challenge for deploying deep neural networks (DNNs) in safety critical settings is the need to provide rigorous ways to quantify their uncertainty. In this paper, we propose a novel algorithm for constructing predicted classification confidences for DNNs that comes with provable correctness guarantees. Our approach uses Clopper-Pearson confidence intervals for the Binomial distribution in conjunction with the histogram binning approach to calibrated prediction. In addition, we demonstrate how our predicted confidences can be used to enable downstream guarantees in two settings: (i) fast DNN inference, where we demonstrate how to compose a fast but inaccurate DNN with an accurate but slow DNN in a rigorous way to improve performance without sacrificing accuracy, and (ii) safe planning, where we guarantee safety when using a DNN to predict whether a given action is safe based on visual observations. In our experiments, we demonstrate that our approach can be used to provide guarantees for state-of-the-art DNNs.
