论文标题
关于深层多样性对分布外检测的有用性
On the Usefulness of Deep Ensemble Diversity for Out-of-Distribution Detection
论文作者
论文摘要
检测到分布（OOD）数据的能力在深度学习的安全至关重要的应用中很重要。目的是使用从深神经网络中提取的不确定性量度将训练分布中绘制的分布（ID）数据分开。深层合奏是一种公认的方法，可以提高深神经网络产生的不确定性估计的质量，并且与单个模型相比，已证明具有优异的OOD检测性能。文献中现有的直觉是，深度集合预测的多样性表明分布变化，因此应使用诸如相互信息（MI）之类的多样性衡量。我们通过实验表明，与某些OOD数据集中的单模熵相比，使用MI导致MI导致95％fpr@95较差30-40％。我们为Deep Sembles更好的OOD检测性能提出了另一种解释 - OOD检测是二进制分类，我们正在分类分类器。因此，我们表明，通过平均特定于任务的检测得分，例如整体上的能量，可以实现更深入的合奏。
The ability to detect Out-of-Distribution (OOD) data is important in safety-critical applications of deep learning. The aim is to separate In-Distribution (ID) data drawn from the training distribution from OOD data using a measure of uncertainty extracted from a deep neural network. Deep Ensembles are a well-established method of improving the quality of uncertainty estimates produced by deep neural networks, and have been shown to have superior OOD detection performance compared to single models. An existing intuition in the literature is that the diversity of Deep Ensemble predictions indicates distributional shift, and so measures of diversity such as Mutual Information (MI) should be used for OOD detection. We show experimentally that this intuition is not valid on ImageNet-scale OOD detection -- using MI leads to 30-40% worse %FPR@95 compared to single-model entropy on some OOD datasets. We suggest an alternative explanation for Deep Ensembles' better OOD detection performance -- OOD detection is binary classification and we are ensembling diverse classifiers. As such we show that practically, even better OOD detection performance can be achieved for Deep Ensembles by averaging task-specific detection scores such as Energy over the ensemble.
