Deploying machine learning in open environments presents the challenge of
encountering diverse test inputs that differ significantly from the training
data. These out-of-distribution samples may exhibit shifts in local or global
features compared to the training distribution. The machine learning (ML)
community has responded with a number of methods aimed at distinguishing
anomalous inputs from original training data. However, the majority of previous
studies have primarily focused on the output layer or penultimate layer of
pre-trained deep neural networks. In this paper, we propose a novel framework,
Multitesting-based Layer-wise Out-of-Distribution (OOD) Detection (MLOD), to
identify distributional shifts in test samples at different levels of features
through rigorous multiple testing procedure. Our approach distinguishes itself
from existing methods as it does not require modifying the structure or
fine-tuning of the pre-trained classifier. Through extensive experiments, we
demonstrate that our proposed framework can seamlessly integrate with any
existing distance-based inspection method while efficiently utilizing feature
extractors of varying depths. Our scheme effectively enhances the performance
of out-of-distribution detection when compared to baseline methods. In
particular, MLOD-Fisher achieves superior performance in general. When trained
using KNN on CIFAR10, MLOD-Fisher significantly lowers the false positive rate
(FPR) from 24.09% to 7.47% on average compared to merely utilizing the features
of the last layer.

提出一种名为多测试层级异分布检测（MLOD）的框架，通过严格的多测试程序，在不需要修改或微调预训练分类器的结构的情况下，能够在不同特征层级上识别测试样本中的分布偏移，并通过有效利用不同深度的特征提取器，有效提高异分布检测性能。