  Out-of-distribution (OOD) detection is essential when deploying neural
networks in the real world. One main challenge is that neural networks often
make overconfident predictions on OOD data. In this study, we propose an
effective post-hoc OOD detection method based on a new feature masking strategy
and a novel logit smoothing strategy. Feature masking determines the important
features at the penultimate layer for each in-distribution (ID) class based on
the weights of the ID class in the classifier head and masks the rest features.
Logit smoothing computes the cosine similarity between the feature vector of
the test sample and the prototype of the predicted ID class at the penultimate
layer and uses the similarity as an adaptive temperature factor on the logit to
alleviate the network's overconfidence prediction for OOD data. With these
strategies, we can reduce feature activation of OOD data and enlarge the gap in
OOD score between ID and OOD data. Extensive experiments on multiple standard
OOD detection benchmarks demonstrate the effectiveness of our method and its
compatibility with existing methods, with new state-of-the-art performance
achieved from our method. The source code will be released publicly.
