Out-of-Distribution Detection using Neural Activation Prior

Out-of-distribution detection is a crucial technique for deploying machine learning models in the real world to handle the unseen scenarios. In this paper, we propose a simple but effective Neural Activation Prior (NAP) for out-of-distribution detection (OOD). 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 of its neurons being activated with a larger response by an in-distribution (ID) sample is significantly higher than that by an OOD sample. An intuitive explanation is each channel in a model fully trained on ID dataset would play a role in detecting a certain pattern in the samples within the ID dataset, and a few neurons can be activated with a large response when the pattern is detected in an input sample. Thus, a new scoring function based on this prior is proposed to highlight the role of these strongly activated neurons in OOD detection. This approach is plug-and-play and does not lead to any performance degradation on in-distribution 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-10, CIFAR-100 and ImageNet datasets, which demonstrates the power of the proposed prior.