A Unified Framework for Detecting Out-of-Distribution and Adversarial Samples

Detecting test samples drawn sufficiently far away from the training distribution
statistically or adversarially is a fundamental requirement for deploying a good
classifier in many real-world machine learning applications. However, deep neural networks with the softmax classifier are known to produce highly overconfident
posterior distributions even for such abnormal samples. In this paper, we propose
a simple yet effective method for detecting any abnormal samples, which is applicable to any pre-trained softmax neural classifier. We obtain the class conditional
Gaussian distributions with respect to (low- and upper-level) features of the deep
models under Gaussian discriminant analysis, which result in a confidence score
based on the Mahalanobis distance. While most prior methods have been evaluated for detecting either out-of-distribution or adversarial samples, but not both,
the proposed method achieves the state-of-the-art performances for both cases in
our experiments. Moreover, we found that our proposed method is more robust
in harsh cases, e.g., when the training dataset has noisy labels or small number of
samples. Finally, we show that the proposed method enjoys broader usage by applying it to class-incremental learning: whenever out-of-distribution samples are
detected, our classification rule can incorporate new classes well without further
training deep models.

• Machine Intelligence