  Detecting out-of-distribution (OOD) samples is crucial to the safe deployment
of a classifier in the real world. However, deep neural networks are known to
be overconfident for abnormal data. Existing works directly design score
function by mining the inconsistency from classifier for in-distribution (ID)
and OOD. In this paper, we further complement this inconsistency with
reconstruction error, based on the assumption that an autoencoder trained on ID
data can not reconstruct OOD as well as ID. We propose a novel method, READ
(Reconstruction Error Aggregated Detector), to unify inconsistencies from
classifier and autoencoder. Specifically, the reconstruction error of raw
pixels is transformed to latent space of classifier. We show that the
transformed reconstruction error bridges the semantic gap and inherits
detection performance from the original. Moreover, we propose an adjustment
strategy to alleviate the overconfidence problem of autoencoder according to a
fine-grained characterization of OOD data. Under two scenarios of pre-training
and retraining, we respectively present two variants of our method, namely
READ-MD (Mahalanobis Distance) only based on pre-trained classifier and READ-ED
(Euclidean Distance) which retrains the classifier. Our methods do not require
access to test time OOD data for fine-tuning hyperparameters. Finally, we
demonstrate the effectiveness of the proposed methods through extensive
comparisons with state-of-the-art OOD detection algorithms. On a CIFAR-10
pre-trained WideResNet, our method reduces the average FPR@95TPR by up to 9.8%
compared with previous state-of-the-art.
