论文标题
概率邻域组件分析：深度学习中样本有效的不确定性估计
Probabilistic Neighbourhood Component Analysis: Sample Efficient Uncertainty Estimation in Deep Learning
论文作者
论文摘要
尽管深层神经网络（DNNS）在各种应用中达到了最新的准确性，但它们通常无法准确估算其预测性不确定性，而反过来又无法认识到这些预测何时可能是错误的。文献中提出了几种不确定性感知模型，例如贝叶斯神经网络（BNN）和深层集合，以量化预测性不确定性。但是，该领域的研究主要仅限于大数据制度。在这项工作中，我们表明，当训练数据的量很小时，最先进的BNN和深层集成模型的不确定性估计能力会大大降低。为了解决小型数据制度中准确的不确定性估计问题的问题，我们提出了对流行的样本效率非参数KNN方法的概率概括。我们的方法使深度KNN分类器能够准确量化其预测中的基本不确定性。与最新的胸部X射线诊断相比，我们通过实现了较高的不确定性定量来证明所提出的方法的有用性。我们的代码可在https://github.com/ankurmallick/sample-felficity-uq上找到
While Deep Neural Networks (DNNs) achieve state-of-the-art accuracy in various applications, they often fall short in accurately estimating their predictive uncertainty and, in turn, fail to recognize when these predictions may be wrong. Several uncertainty-aware models, such as Bayesian Neural Network (BNNs) and Deep Ensembles have been proposed in the literature for quantifying predictive uncertainty. However, research in this area has been largely confined to the big data regime. In this work, we show that the uncertainty estimation capability of state-of-the-art BNNs and Deep Ensemble models degrades significantly when the amount of training data is small. To address the issue of accurate uncertainty estimation in the small-data regime, we propose a probabilistic generalization of the popular sample-efficient non-parametric kNN approach. Our approach enables deep kNN classifier to accurately quantify underlying uncertainties in its prediction. We demonstrate the usefulness of the proposed approach by achieving superior uncertainty quantification as compared to state-of-the-art on a real-world application of COVID-19 diagnosis from chest X-Rays. Our code is available at https://github.com/ankurmallick/sample-efficient-uq
