论文标题
一个简单的框架，用于量化深层神经网络中不同类型的不确定性进行图像分类
A Simple Framework to Quantify Different Types of Uncertainty in Deep Neural Networks for Image Classification
论文作者
论文摘要
量化模型预测中的不确定性很重要，因为它可以通过以知情方式对模型的输出来提高AI系统的安全性。这对于错误成本很高的应用至关重要，例如自动驾驶汽车控制，医疗图像分析，财务估计或法律领域。深度神经网络是最近在各种任务上实现最先进的表现的强大预测指标。量化DNN中的预测不确定性是一个具有挑战性但仍在进行的问题。在本文中，我们提出了一个完整的框架，以捕获DNN中的三种已知类型的不确定性，以完成图像分类的任务。该框架包括用于模型不确定性的CNN集合，这是一种监督的重建自动编码器，以捕获分布不确定性并使用网络最后一层中激活功能的输出，以捕获数据不确定性。最后，我们演示了我们在流行图像数据集中进行分类的方法的效率。
Quantifying uncertainty in a model's predictions is important as it enables the safety of an AI system to be increased by acting on the model's output in an informed manner. This is crucial for applications where the cost of an error is high, such as in autonomous vehicle control, medical image analysis, financial estimations or legal fields. Deep Neural Networks are powerful predictors that have recently achieved state-of-the-art performance on a wide spectrum of tasks. Quantifying predictive uncertainty in DNNs is a challenging and yet on-going problem. In this paper we propose a complete framework to capture and quantify three known types of uncertainty in DNNs for the task of image classification. This framework includes an ensemble of CNNs for model uncertainty, a supervised reconstruction auto-encoder to capture distributional uncertainty and using the output of activation functions in the last layer of the network, to capture data uncertainty. Finally we demonstrate the efficiency of our method on popular image datasets for classification.
