  Efficient and effective Out-of-Distribution (OOD) detection is essential for
the safe deployment of AI in latency-critical applications. Recently, studies
have revealed that detecting OOD based on feature space information can be
highly effective. Despite their effectiveness, however, exiting feature space
OOD methods may incur non-negligible computational overhead, given their
reliance on auxiliary models built from training features. In this paper, we
aim to obviate auxiliary models to optimize computational efficiency while
leveraging the rich information embedded in the feature space. We investigate
from the novel perspective of decision boundaries and propose to detect OOD
using the feature distance to decision boundaries. To minimize the cost of
measuring the distance, we introduce an efficient closed-form estimation,
analytically proven to tightly lower bound the distance. We observe that ID
features tend to reside further from the decision boundaries than OOD features.
Our observation aligns with the intuition that models tend to be more decisive
on ID samples, considering that distance to decision boundaries quantifies
model uncertainty. From our understanding, we propose a hyperparameter-free,
auxiliary model-free OOD detector. Our OOD detector matches or surpasses the
effectiveness of state-of-the-art methods across extensive experiments.
Meanwhile, our OOD detector incurs practically negligible overhead in inference
latency. Overall, we significantly enhance the efficiency-effectiveness
trade-off in OOD detection.

Machine Learning;Image and Video Processing