Efficient out-of-distribution detection via layer-adaptive scoring and early stopping.

Introduction: Multi-layer aggregation is key to the success of out-of-distribution (OOD) detection in deep neural networks. Moreover, in real-time systems, the efficiency of OOD detection is equally important as its effectiveness.

Methods: We propose a novel early stopping OOD detection framework for deep neural networks. By attaching multiple OOD detectors to the intermediate layers, this framework can detect OODs early to save computational cost. Additionally, through a layer-adaptive scoring function, it can adaptively select the optimal layer for each OOD based on its complexity, thereby improving OOD detection accuracy.

Results: Extensive experiments demonstrate that our proposed framework is robust against OODs of varying complexity. Adopting the early stopping strategy can increase OOD detection efficiency by up to 99.1% while maintaining superior accuracy.

Discussion: OODs of varying complexity are better detected at different layers. Leveraging the intrinsic characteristics of inputs encoded in the intermediate latent space is important for achieving high OOD detection accuracy. Our proposed framework, incorporating early stopping, significantly enhances OOD detection efficiency without compromising accuracy, making it practical for real-time applications.