  Out-of-distribution (OOD) detection is essential to prevent anomalous inputs
from causing a model to fail during deployment. While improved OOD detection
methods have emerged, they often rely on the final layer outputs and require a
full feedforward pass for any given input. In this paper, we propose a novel
framework, multi-level out-of-distribution detection MOOD, which exploits
intermediate classifier outputs for dynamic and efficient OOD inference. We
explore and establish a direct relationship between the OOD data complexity and
optimal exit level, and show that easy OOD examples can be effectively detected
early without propagating to deeper layers. At each exit, the OOD examples can
be distinguished through our proposed adjusted energy score, which is both
empirically and theoretically suitable for networks with multiple classifiers.
We extensively evaluate MOOD across 10 OOD datasets spanning a wide range of
complexities. Experiments demonstrate that MOOD achieves up to 71.05%
computational reduction in inference, while maintaining competitive OOD
detection performance.
