Recent advances in out-of-distribution (OOD) detection on image data show
that pre-trained neural network classifiers can separate in-distribution (ID)
from OOD data well, leveraging the class-discriminative ability of the model
itself. Methods have been proposed that either use logit information directly
or that process the model's penultimate layer activations. With "WeiPer", we
introduce perturbations of the class projections in the final fully connected
layer which creates a richer representation of the input. We show that this
simple trick can improve the OOD detection performance of a variety of methods
and additionally propose a distance-based method that leverages the properties
of the augmented WeiPer space. We achieve state-of-the-art OOD detection
results across multiple benchmarks of the OpenOOD framework, especially
pronounced in difficult settings in which OOD samples are positioned close to
the training set distribution. We support our findings with theoretical
motivations and empirical observations, and run extensive ablations to provide
insights into why WeiPer works.

通过引入最后全连接层的类别投影的扰动，我们展示了这个简单的技巧可以提高多种方法的离群值检测性能，并提出了一种基于距离的方法来利用增强的 WeiPer 空间的属性。在 OpenOOD 框架的多个基准测试中，我们实现了最先进的离群值检测结果，特别是在离群样本接近训练集分布的困难情况下。我们通过理论动机和实证观察支持我们的发现，并进行了广泛的消融实验，以提供关于为什么 WeiPer 起作用的见解。