Learning Non-Linear Invariants for Unsupervised Out-of-Distribution Detection
<|reference_start|>Learning Non-Linear Invariants for Unsupervised Out-of-Distribution Detection: 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.<|reference_end|>
@article{doorenbos2024learning,
    title={Learning Non-Linear Invariants for Unsupervised Out-of-Distribution
  Detection},
    author={Lars Doorenbos, Raphael Sznitman, Pablo M'arquez-Neila},
    journal={arXiv preprint arXiv:2407.04022},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.04022},
    primaryClass={cs.CV cs.AI cs.LG}
}