  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.
