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Classification-Based Anomaly Detection for General Data

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arxiv 2005.02359 v1 pith:SUALEANU submitted 2020-05-05 cs.LG cs.CVstat.ML

Liron Bergman , Yedid Hoshen This is my paper
classification cs.LGcs.CVstat.ML
keywords datamethodanomalyclassification-baseddetectionmethodsaccuracyachieve
verification ladder T0 review T1 audit T2 compute T3 formal
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Anomaly detection, finding patterns that substantially deviate from those seen previously, is one of the fundamental problems of artificial intelligence. Recently, classification-based methods were shown to achieve superior results on this task. In this work, we present a unifying view and propose an open-set method, GOAD, to relax current generalization assumptions. Furthermore, we extend the applicability of transformation-based methods to non-image data using random affine transformations. Our method is shown to obtain state-of-the-art accuracy and is applicable to broad data types. The strong performance of our method is extensively validated on multiple datasets from different domains.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. CORE: In-Context Reconstruction for Unified Tabular Anomaly Detection

    cs.AI 2026-07 conditional novelty 6.0 of 10

    CORE detects anomalies in new tabular datasets by reconstructing each test sample from the nearest normal context samples in a learned, feature-aligned space; it is proposed as the first reconstruction-based unified t...

  2. ReTabAD: A Benchmark for Restoring Semantic Context in Tabular Anomaly Detection

    cs.AI 2025-10 conditional novelty 6.0 of 10

    A new 20-dataset benchmark with textual metadata reports that zero-shot LLMs detect tabular anomalies better when given semantic context, but label descriptions embedded in the metadata may explain much of the gain.

  3. Investigating Mask-aware Prototype Learning for Tabular Anomaly Detection

    cs.LG 2025-06 conditional novelty 5.0 of 10

    A new tabular anomaly detection model, PTAD, combines learnable masks in two spaces with optimal transport distances to learned prototypes and reports the best average AUC-PR and AUC-ROC on 20 benchmarks.

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