  Efficient and effective Out-of-Distribution (OOD) detection is essential for
the safe deployment of AI systems. Existing feature space methods, while
effective, often incur significant computational overhead due to their reliance
on auxiliary models built from training features. In this paper, we propose a
computationally-efficient OOD detector without using auxiliary models while
still leveraging the rich information embedded in the feature space.
Specifically, we detect OOD samples based on their feature distances to
decision boundaries. To minimize computational cost, we introduce an efficient
closed-form estimation, analytically proven to tightly lower bound the
distance. Based on our estimation, we discover that In-Distribution (ID)
features tend to be further from decision boundaries than OOD features.
Additionally, ID and OOD samples are better separated when compared at equal
deviation levels from the mean of training features. By regularizing the
distances to decision boundaries based on feature deviation from the mean, we
develop a hyperparameter-free, auxiliary model-free OOD detector. Our method
matches or surpasses the effectiveness of state-of-the-art methods in extensive
experiments while incurring negligible overhead in inference latency. Overall,
our approach significantly improves the efficiency-effectiveness trade-off in
OOD detection. Code is available at: https://github.com/litianliu/fDBD-OOD.
