Efficient out-of-distribution detection via layer-adaptive scoring and early stopping
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.
Authors
- Feng Chen (ORCID: https://orcid.org/0000-0002-4508-5963)
- Chen Zhao (ORCID: https://orcid.org/0000-0002-6400-0048)
- Haoliang Wang (ORCID: https://orcid.org/0000-0003-4222-9893)
Institutions
- Baylor University (US)
- The University of Texas at Dallas (US)
Publication Details
- Journal
- Frontiers in Big Data
- Published
- 2024-11-20
- DOI
- https://doi.org/10.3389/fdata.2024.1444634
- Citations
- 1
- Primary Topic
- Anomaly Detection Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.35