  Deep neural networks (DNNs) are known to produce incorrect predictions with
very high confidence on out-of-distribution (OOD) inputs. This limitation is
one of the key challenges in the adoption of deep learning models in
high-assurance systems such as autonomous driving, air traffic management, and
medical diagnosis. This challenge has received significant attention recently,
and several techniques have been developed to detect inputs where the model's
prediction cannot be trusted. These techniques use different statistical,
geometric, or topological signatures. This paper presents a taxonomy of OOD
outlier inputs based on their source and nature of uncertainty. We demonstrate
how different existing detection approaches fail to detect certain types of
outliers. We utilize these insights to develop a novel integrated detection
approach that uses multiple attributes corresponding to different types of
outliers. Our results include experiments on CIFAR10, SVHN and MNIST as
in-distribution data and Imagenet, LSUN, SVHN (for CIFAR10), CIFAR10 (for
SVHN), KMNIST, and F-MNIST as OOD data across different DNN architectures such
as ResNet34, WideResNet, DenseNet, and LeNet5.
