Look Around and Find Out: OOD Detection with Relative Angles
<|reference_start|>Look Around and Find Out: OOD Detection with Relative Angles: Deep learning systems deployed in real-world applications often encounter data that is different from their in-distribution (ID). A reliable system should ideally abstain from making decisions in this out-of-distribution (OOD) setting. Existing state-of-the-art methods primarily focus on feature distances, such as k-th nearest neighbors and distances to decision boundaries, either overlooking or ineffectively using in-distribution statistics. In this work, we propose a novel angle-based metric for OOD detection that is computed relative to the in-distribution structure. We demonstrate that the angles between feature representations and decision boundaries, viewed from the mean of in-distribution features, serve as an effective discriminative factor between ID and OOD data. Our method achieves state-of-the-art performance on CIFAR-10 and ImageNet benchmarks, reducing FPR95 by 0.88% and 7.74% respectively. Our score function is compatible with existing feature space regularization techniques, enhancing performance. Additionally, its scale-invariance property enables creating an ensemble of models for OOD detection via simple score summation.<|reference_end|>
@article{demirel2024look,
    title={Look Around and Find Out: OOD Detection with Relative Angles},
    author={Berker Demirel, Marco Fumero, Francesco Locatello},
    journal={arXiv preprint arXiv:2410.04525},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.04525},
    primaryClass={cs.LG cs.CV}
}