Doorenbos, Lars; Sznitman, Raphael; Márquez-Neila, Pablo (October 2024). Learning non-linear invariants for unsupervised out-of-distribution detection. In: European Conference on Computer Vision.
Text
2407.04022v1.pdf - Published Version Restricted to registered users only Available under License Publisher holds Copyright. Download (2MB) |
The inability of deep learning models to handle data drawn from unseen distributions has sparked much interest in unsupervised out-of-distribution (U-OOD) detection, as it is crucial for reliable deep learning models. Despite considerable attention, theoretically-motivated approaches are few and far between, with most methods building on top of some form of heuristic. Recently, U-OOD was formalized in the context of data invariants, allowing a clearer understanding of how to characterize U-OOD, and methods leveraging affine invariants have attained state-of-the-art results on large-scale benchmarks. Nevertheless, the restriction to affine invariants hinders the expressiveness of the approach. In this work, we broaden the affine invariants formulation to a more general case and propose a framework consisting of a normalizing flow-like architecture capable of learning non-linear invariants. Our novel approach achieves state-of-the-art results on an extensive U-OOD benchmark, and we demonstrate its further applicability to tabular data. Finally, we show our method has the same desirable properties as those based on affine invariants.
Item Type: |
Conference or Workshop Item (Paper) |
---|---|
Division/Institute: |
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research 10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - AI in Medical Imaging Laboratory |
Graduate School: |
Graduate School for Cellular and Biomedical Sciences (GCB) |
UniBE Contributor: |
Doorenbos, Lars Jelte, Sznitman, Raphael, Márquez Neila, Pablo |
Subjects: |
500 Science > 570 Life sciences; biology 600 Technology > 610 Medicine & health 000 Computer science, knowledge & systems 600 Technology |
Language: |
English |
Submitter: |
Lars Jelte Doorenbos |
Date Deposited: |
16 Jul 2024 11:38 |
Last Modified: |
16 Jul 2024 11:38 |
ArXiv ID: |
2407.04022v1 |
BORIS DOI: |
10.48350/199035 |
URI: |
https://boris.unibe.ch/id/eprint/199035 |