ARE ALL OUTLIERS ALIKE? ON UNDERSTANDING THE DIVERSITY OF OUTLIERS FOR DETECTING OODS

Abstract

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 CI-FAR10, 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.

1. INTRODUCTION

Deep neural networks (DNNs) have achieved remarkable performance-levels in many areas such as computer vision (Gkioxari et al., 2015) , speech recognition (Hannun et al., 2014) , and text analysis (Majumder et al., 2017) . But their deployment in the safety-critical systems such as self-driving vehicles (Bojarski et al., 2016) , aircraft collision avoidance (Julian & Kochenderfer, 2017) , and medical diagnoses (De Fauw et al., 2018) is hindered by their brittleness. One major challenge is the inability of DNNs to be self-aware of when new inputs are outside the training distribution and likely to produce incorrect predictions. It has been widely reported in literature (Guo et al., 2017a; Hendrycks & Gimpel, 2016 ) that deep neural networks exhibit overconfident incorrect predictions on inputs which are outside the training distribution. The responsible deployment of deep neural network models in high-assurance applications necessitates detection of out-of-distribution (OOD) data so that DNNs can abstain from making decisions on those. Recent approaches for OOD detection consider different statistical, geometric or topological signatures in data that differentiate OODs from the training distribution. For example, the changes in the softmax scores due to input perturbations and temperature scaling have been used to detect OODs (Hendrycks & Gimpel, 2016; Liang et al., 2017; Guo et al., 2017b) . Papernot & McDaniel (2018) 2015) , likelihood-ratio between the in-distribution and OOD samples (Ren et al., 2019) , trust scores (ratio of the distance to the nearest class different from the predicted class and the distance to the predicted class) (Jiang et al., 2018) , density function (Liu et al., 2020; Hendrycks et al., 2019a) , probability distribution of the softmax scores (Lee et al., 2017; Hendrycks et al., 2019b; Tack et al., 2020; Hendrycks et al., 2019a) have also been used to detect OODs. All these methods attempt to develop a uniform approach with a single signature to detect all OODs accompanied by empirical 1



use the conformance among the labels of the nearest neighbors while Tack et al. (2020) use cosine similarity (modulated by the norm of the feature vector) to the nearest training sample for the detection of OODs. Lee et al. (2018) consider the Mahalanobis distance of an input from the in-distribution data to detect OODs. Several other metrics such as reconstruction error (An & Cho,

