  It is important to quantify the uncertainty of input samples, especially in
mission-critical domains such as autonomous driving and healthcare, where
failure predictions on out-of-distribution (OOD) data are likely to cause big
problems. OOD detection problem fundamentally begins in that the model cannot
express what it is not aware of. Post-hoc OOD detection approaches are widely
explored because they do not require an additional re-training process which
might degrade the model's performance and increase the training cost. In this
study, from the perspective of neurons in the deep layer of the model
representing high-level features, we introduce a new aspect for analyzing the
difference in model outputs between in-distribution data and OOD data. We
propose a novel method, Leveraging Important Neurons (LINe), for post-hoc Out
of distribution detection.
  Shapley value-based pruning reduces the effects of noisy outputs by selecting
only high-contribution neurons for predicting specific classes of input data
and masking the rest. Activation clipping fixes all values above a certain
threshold into the same value, allowing LINe to treat all the class-specific
features equally and just consider the difference between the number of
activated feature differences between in-distribution and OOD data.
Comprehensive experiments verify the effectiveness of the proposed method by
outperforming state-of-the-art post-hoc OOD detection methods on CIFAR-10,
CIFAR-100, and ImageNet datasets.
