  Out-of-distribution detection (OOD) is a crucial technique for deploying
machine learning models in the real world to handle the unseen scenarios. In
this paper, we first propose a simple yet effective Neural Activation Prior
(NAP) for OOD detection. Our neural activation prior is based on a key
observation that, for a channel before the global pooling layer of a fully
trained neural network, the probability of a few neurons being activated with a
large response by an in-distribution (ID) sample is significantly higher than
that by an OOD sample. An intuitive explanation is that for a model fully
trained on ID dataset, each channel would play a role in detecting a certain
pattern in the ID dataset, and a few neurons can be activated with a large
response when the pattern is detected in an input sample. Then, a new scoring
function based on this prior is proposed to highlight the role of these
strongly activated neurons in OOD detection. Our approach is plug-and-play and
does not lead to any performance degradation on ID data classification and
requires no extra training or statistics from training or external datasets.
Notice that previous methods primarily rely on post-global-pooling features of
the neural networks, while the within-channel distribution information we
leverage would be discarded by the global pooling operator. Consequently, our
method is orthogonal to existing approaches and can be effectively combined
with them in various applications. Experimental results show that our method
achieves the state-of-the-art performance on CIFAR benchmark and ImageNet
dataset, which demonstrates the power of the proposed prior. Finally, we extend
our method to Transformers and the experimental findings indicate that NAP can
also significantly enhance the performance of OOD detection on Transformers,
thereby demonstrating the broad applicability of this prior knowledge.
