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Rethinking Assumptions in Deep Anomaly Detection

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arxiv 2006.00339 v3 pith:QCRPMH22 submitted 2020-05-30 cs.LG stat.ML

Lukas Ruff , Robert A. Vandermeulen , Billy Joe Franks , Klaus-Robert Müller , Marius Kloft This is my paper · ORCID
classification cs.LGstat.ML
keywords deepanomalousanomalydetectionimagesableaccessanomalies
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Though anomaly detection (AD) can be viewed as a classification problem (nominal vs. anomalous) it is usually treated in an unsupervised manner since one typically does not have access to, or it is infeasible to utilize, a dataset that sufficiently characterizes what it means to be "anomalous." In this paper we present results demonstrating that this intuition surprisingly seems not to extend to deep AD on images. For a recent AD benchmark on ImageNet, classifiers trained to discern between normal samples and just a few (64) random natural images are able to outperform the current state of the art in deep AD. Experimentally we discover that the multiscale structure of image data makes example anomalies exceptionally informative.

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Cited by 2 Pith papers

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  1. INP-Former++: Advancing Universal Anomaly Detection via Intrinsic Normal Prototypes and Residual Learning

    cs.CV 2025-06 conditional novelty 6.0 of 10

    INP-Former++ detects image defects by extracting intrinsic normal prototypes from the test image itself and reconstructing only normal regions, achieving state-of-the-art results across single-class, multi-class, few-...

  2. CAPMix: Robust KPI Anomaly Detection for AIOps in Noisy and Dynamic Environments

    cs.LG 2025-09 conditional novelty 4.0 of 10

    CAPMix combines CutAddPaste anomaly injection, DTW-based label revision, and dual-space mixup to improve time-series anomaly detection, reporting gains over prior methods on five benchmarks.

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