Neural networks are known to produce over-confident predictions on input
images, even when these images are out-of-distribution (OOD) samples. This
limits the applications of neural network models in real-world scenarios, where
OOD samples exist. Many existing approaches identify the OOD instances via
exploiting various cues, such as finding irregular patterns in the feature
space, logits space, gradient space or the raw space of images. In contrast,
this paper proposes a simple Test-time Linear Training (ETLT) method for OOD
detection. Empirically, we find that the probabilities of input images being
out-of-distribution are surprisingly linearly correlated to the features
extracted by neural networks. To be specific, many state-of-the-art OOD
algorithms, although designed to measure reliability in different ways,
actually lead to OOD scores mostly linearly related to their image features.
Thus, by simply learning a linear regression model trained from the paired
image features and inferred OOD scores at test-time, we can make a more precise
OOD prediction for the test instances. We further propose an online variant of
the proposed method, which achieves promising performance and is more practical
in real-world applications. Remarkably, we improve FPR95 from $51.37\%$ to
$12.30\%$ on CIFAR-10 datasets with maximum softmax probability as the base OOD
detector. Extensive experiments on several benchmark datasets show the efficacy
of ETLT for OOD detection task.

本文提出了一种基于线性回归的测试时间训练方法，将输入图像的特征与其推断出的异常检测 (OOD) 分数进行配对，并在实现更精确的 OOD 检测上取得了很好的效果。