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arxiv: 2604.18266 · v1 · submitted 2026-04-20 · 💻 cs.AI

Recognition: unknown

Enhancing Tabular Anomaly Detection via Pseudo-Label-Guided Generation

Wei Huang , Yuxuan Xiong , Hezhe Qiao , Yu-Ming Shang , Xiangling Fu , Guansong Pang
Authors on Pith no claims yet

Pith reviewed 2026-05-10 03:53 UTC · model grok-4.3

classification 💻 cs.AI
keywords tabular anomaly detectionpseudo-label guided generationfeature-level abnormalitiessynthetic anomaliestwo-stage data selectionunsupervised detectorslocalized anomaly patternsF1 score improvement
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The pith

PLAG generates pseudo-anomalies from initial pseudo-labels and filters them to capture localized feature abnormalities, improving tabular anomaly detection without ground-truth labels.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes PLAG to address the lack of labeled anomalies in tabular data by creating synthetic anomalies that highlight per-feature deviations rather than whole-sample scores. It starts with rough pseudo-labels to guide generation, then applies format checks and uncertainty scoring to keep only reliable and varied examples. These examples act as training signals that help models distinguish normal from anomalous rows more accurately. Experiments show the approach beats eight existing methods and can be plugged into current unsupervised detectors to raise their F1 scores. A sympathetic reader would care because it turns a common data-scarcity problem into a practical way to focus on the exact features that make a row unusual.

Core claim

By utilizing pseudo-anomalies as guidance signals and decoupling the overall anomaly quantification of a sample into an accumulation of feature-level abnormalities, PLAG not only effectively obviates the need for scarce ground-truth labels but also provides a novel perspective for the model to comprehend localized anomalous signals at a fine-grained level. Furthermore, a two-stage data selection strategy is proposed, integrating format verification and uncertainty estimation to rigorously filter candidate samples, thereby ensuring the fidelity and diversity of the synthetic anomalies. Ultimately, these filtered synthetic anomalies serve as robust discriminative guidance, empowering the model

What carries the argument

Pseudo-label-guided generation that decomposes anomaly scores into summed feature-level abnormalities, followed by two-stage filtering that combines format verification with uncertainty estimation.

If this is right

  • PLAG achieves state-of-the-art performance against eight representative baselines.
  • As a flexible framework, it integrates seamlessly with existing unsupervised detectors and consistently boosts F1-scores by 0.08 to 0.21.
  • The method avoids the global computation of anomalies that overlooks localized feature patterns.
  • Filtered synthetic anomalies provide discriminative guidance that separates normal and anomalous instances more effectively.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If feature-level decomposition proves reliable, the same generation-and-filter pipeline could be tested on sequence or graph data where local deviations also matter.
  • Gains may depend on how well the base detector's initial pseudo-labels align with true anomaly locations.
  • Extending the uncertainty filter to track changes over time could help when tabular data arrives in streams.
  • Pairing the generated samples with contrastive objectives might further sharpen the boundary between normal and anomalous rows.

Load-bearing premise

The assumption that pseudo-anomalies created from initial pseudo-labels accurately reflect localized feature-level anomaly patterns and that format verification plus uncertainty filtering removes bias without losing useful variety.

What would settle it

Apply PLAG to a tabular dataset engineered so that anomalies are uniform across all features rather than concentrated in specific ones, and check whether the reported gains over baselines vanish.

Figures

Figures reproduced from arXiv: 2604.18266 by Guansong Pang, Hezhe Qiao, Wei Huang, Xiangling Fu, Yu-Ming Shang, Yuxuan Xiong.

Figure 1
Figure 1. Figure 1: Visualization of Anomaly Generation. (a) Distribution of synthetic [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The framework of PLAG. (1) Pseudo-Label-Guided Candidate Anomaly Generation. PLAG initially employs an unsupervised detector to score [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Tabular-specific prompt template for anomaly generation. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Average performance metrics of various methods across all datasets. [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: The performance gains introduced by PLAG across the diverse [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: 2D visualization comparing the distributions of synthetic anomalies generated by various methods against ground-truth normal and anomalous samples [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗
read the original abstract

Identifying anomalous instances in tabular data is essential for improving data reliability and maintaining system stability. Due to the scarcity of ground-truth anomaly labels, existing methods mainly rely on unsupervised anomaly detection models, or exploit a small number of labeled anomalies to facilitate detection via sample generation or contrastive learning. However, unsupervised methods lack sufficient anomaly awareness, while current generation and contrastive approaches tend to compute anomalies globally, overlooking the localized anomaly patterns of tabular features, resulting in suboptimal detection performance. To address these limitations, we propose PLAG, a pseudo-label-guided anomaly generation method designed to enhance tabular anomaly detection. Specifically, by utilizing pseudo-anomalies as guidance signals and decoupling the overall anomaly quantification of a sample into an accumulation of feature-level abnormalities, PLAG not only effectively obviates the need for scarce ground-truth labels but also provides a novel perspective for the model to comprehend localized anomalous signals at a fine-grained level. Furthermore, a two-stage data selection strategy is proposed, integrating format verification and uncertainty estimation to rigorously filter candidate samples, thereby ensuring the fidelity and diversity of the synthetic anomalies. Ultimately, these filtered synthetic anomalies serve as robust discriminative guidance, empowering the model to better separate normal and anomalous instances. Extensive experiments demonstrate that PLAG achieves state-of-the-art performance against eight representative baselines. Moreover, as a flexible framework, it integrates seamlessly with existing unsupervised detectors, consistently boosting F1-scores by 0.08 to 0.21.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 1 minor

Summary. The paper proposes PLAG, a pseudo-label-guided anomaly generation method for tabular anomaly detection. It uses pseudo-anomalies from unsupervised detectors as guidance to generate synthetic anomalies by decoupling sample-level anomaly quantification into an accumulation of feature-level abnormalities. A two-stage selection strategy combining format verification and uncertainty estimation filters the generated samples to ensure fidelity and diversity. These synthetic anomalies then serve as discriminative guidance to improve separation of normal and anomalous instances. The paper claims state-of-the-art performance against eight baselines and consistent F1-score boosts of 0.08 to 0.21 when integrated with existing unsupervised detectors.

Significance. If the results hold, this would represent a meaningful advance in tabular anomaly detection by providing a label-free mechanism to inject localized, feature-level anomaly signals into models, addressing a noted limitation of global anomaly computation in prior generation-based approaches. The plug-in flexibility with unsupervised detectors is a practical strength that could improve performance in data-reliability applications.

major comments (3)
  1. [Abstract] Abstract: The central claim that PLAG 'decouples the overall anomaly quantification of a sample into an accumulation of feature-level abnormalities' and thereby captures localized patterns is load-bearing for the SOTA and F1-gain results, yet the manuscript provides no independent diagnostic (such as feature-wise anomaly attribution on synthetic versus real anomalies) to confirm that the generated samples exhibit per-feature localization rather than global effects.
  2. [Abstract] Abstract and method description: The two-stage selection (format verification plus uncertainty estimation) is asserted to remove bias from noisy initial pseudo-labels while preserving diversity, but without details on the uncertainty estimator, selection thresholds, or ablation on the filtering steps, it is unclear whether this process recovers missing localized signal or merely discards failures.
  3. [Abstract] Abstract: The reported SOTA performance and 0.08–0.21 F1 gains rest on unshown experimental details, ablations, and error analysis; given the reliance on pseudo-label quality and generation hyperparameters, the absence of these elements undermines assessment of whether the localized-guidance mechanism is responsible for the gains.
minor comments (1)
  1. [Abstract] The F1 improvement range is stated without reference to specific datasets, detectors, or number of runs, reducing clarity on the consistency claim.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and positive review, which highlights both the potential significance of PLAG and areas where the manuscript can be strengthened. We address each major comment point by point below and commit to revisions that directly respond to the concerns.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that PLAG 'decouples the overall anomaly quantification of a sample into an accumulation of feature-level abnormalities' and thereby captures localized patterns is load-bearing for the SOTA and F1-gain results, yet the manuscript provides no independent diagnostic (such as feature-wise anomaly attribution on synthetic versus real anomalies) to confirm that the generated samples exhibit per-feature localization rather than global effects.

    Authors: We agree that an explicit diagnostic would provide stronger substantiation for the central claim. The manuscript describes the decoupling mechanism in the method but does not include a direct feature-wise comparison. In the revised version we will add a new analysis subsection presenting feature-wise anomaly attribution (e.g., per-feature contribution scores) on both the generated synthetic anomalies and real anomalies, demonstrating that the synthetic samples exhibit localized rather than global effects. revision: yes

  2. Referee: [Abstract] Abstract and method description: The two-stage selection (format verification plus uncertainty estimation) is asserted to remove bias from noisy initial pseudo-labels while preserving diversity, but without details on the uncertainty estimator, selection thresholds, or ablation on the filtering steps, it is unclear whether this process recovers missing localized signal or merely discards failures.

    Authors: We acknowledge that the current description of the two-stage selection is insufficiently detailed. In the revision we will expand the relevant method section to provide the explicit formulation of the uncertainty estimator, the precise selection thresholds, and a dedicated ablation study that isolates the contribution of each filtering stage to fidelity, diversity, and retention of localized anomaly signals. revision: yes

  3. Referee: [Abstract] Abstract: The reported SOTA performance and 0.08–0.21 F1 gains rest on unshown experimental details, ablations, and error analysis; given the reliance on pseudo-label quality and generation hyperparameters, the absence of these elements undermines assessment of whether the localized-guidance mechanism is responsible for the gains.

    Authors: We concur that additional ablations and error analysis are required to isolate the contribution of the localized-guidance mechanism. In the revised experiments section we will add: ablations varying the quality of initial pseudo-labels, sensitivity analysis on generation hyperparameters, and error analysis stratified by dataset and anomaly characteristics. These additions will allow readers to assess whether the observed gains are attributable to the feature-level decoupling. revision: yes

Circularity Check

0 steps flagged

No significant circularity; PLAG's claims rest on empirical validation rather than self-referential derivation.

full rationale

The paper introduces PLAG as a generation framework that starts from pseudo-labels produced by an external unsupervised detector, generates candidates by decoupling anomaly scores into per-feature terms, applies independent format-verification and uncertainty filters, and then reports experimental F1 gains and SOTA rankings against eight baselines. No equations, uniqueness theorems, or fitted parameters are shown to reduce the reported performance improvements to the inputs by construction. The two-stage selection and feature-level decoupling are presented as design choices whose effectiveness is assessed externally via held-out test metrics, not via internal re-derivation or self-citation chains. This is the normal case of a method paper whose central claims are falsifiable through replication on standard benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

Abstract-only review limits visibility; the approach rests on the assumption that pseudo-labels provide usable guidance and that synthetic anomalies can be made sufficiently realistic via selection.

free parameters (1)
  • generation and selection hyperparameters
    Typical ML generation methods require tunable parameters for pseudo-label threshold, generation strength, and uncertainty cutoff; none are quantified here.
axioms (2)
  • domain assumption Pseudo-labels from an initial unsupervised model can serve as reliable guidance for generating localized anomalies
    Central premise stated in the abstract as the way to avoid needing ground-truth labels.
  • domain assumption Anomaly score of a sample can be usefully decomposed into an accumulation of independent feature-level abnormalities
    Explicitly invoked to justify the generation strategy.

pith-pipeline@v0.9.0 · 5573 in / 1359 out tokens · 36323 ms · 2026-05-10T03:53:20.864062+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

36 extracted references · 4 canonical work pages · 1 internal anchor

  1. [1]

    Financial fraud detection ap- plying data mining techniques: A comprehensive review from 2009 to 2019,

    K. G. Al-Hashedi and P. Magalingam, “Financial fraud detection ap- plying data mining techniques: A comprehensive review from 2009 to 2019,”Computer Science Review, vol. 40, p. 100402, 2021

    2009

  2. [2]

    arXiv preprint arXiv:2403.19735 (2024)

    T. Park, “Enhancing anomaly detection in financial markets with an llm- based multi-agent framework,”arXiv preprint arXiv:2403.19735, 2024

    work page arXiv 2024

  3. [3]

    Deep learning for medical anomaly detection–a survey,

    T. Fernando, H. Gammulle, S. Denman, S. Sridharan, and C. Fookes, “Deep learning for medical anomaly detection–a survey,”ACM Com- puting Surveys (CSUR), vol. 54, no. 7, pp. 1–37, 2021

    2021

  4. [4]

    Network intrusion detection system: A systematic study of machine learning and deep learning approaches,

    Z. Ahmad, A. Shahid Khan, C. Wai Shiang, J. Abdullah, and F. Ahmad, “Network intrusion detection system: A systematic study of machine learning and deep learning approaches,”Transactions on Emerging Telecommunications Technologies, vol. 32, no. 1, p. e4150, 2021

    2021

  5. [5]

    Support vector method for novelty detection,

    B. Sch ¨olkopf, R. C. Williamson, A. Smola, J. Shawe-Taylor, and J. Platt, “Support vector method for novelty detection,”Advances in neural information processing systems, vol. 12, 1999

    1999

  6. [6]

    Deep one-class classification,

    L. Ruff, R. Vandermeulen, N. Goernitz, L. Deecke, S. A. Siddiqui, A. Binder, E. M ¨uller, and M. Kloft, “Deep one-class classification,” inInternational conference on machine learning. PMLR, 2018, pp. 4393–4402

    2018

  7. [7]

    Neural transformation learning for deep anomaly detection beyond images,

    C. Qiu, T. Pfrommer, M. Kloft, S. Mandt, and M. Rudolph, “Neural transformation learning for deep anomaly detection beyond images,” in International conference on machine learning. PMLR, 2021, pp. 8703– 8714

    2021

  8. [8]

    Anomaly detection for tabular data with internal contrastive learning,

    T. Shenkar and L. Wolf, “Anomaly detection for tabular data with internal contrastive learning,” inInternational conference on learning representations, 2022

    2022

  9. [9]

    Mcm: Masked cell modeling for anomaly detection in tabular data,

    J. Yin, Y . Qiao, Z. Zhou, X. Wang, and J. Yang, “Mcm: Masked cell modeling for anomaly detection in tabular data,” inThe Twelfth International Conference on Learning Representations, 2024

    2024

  10. [10]

    Unsupervised anomaly detection with rejection,

    L. Perini and J. Davis, “Unsupervised anomaly detection with rejection,” Advances in Neural Information Processing Systems, vol. 36, 2024

    2024

  11. [11]

    Nng-mix: Improving semi-supervised anomaly detection with pseudo-anomaly generation,

    H. Dong, G. Frusque, Y . Zhao, E. Chatzi, and O. Fink, “Nng-mix: Improving semi-supervised anomaly detection with pseudo-anomaly generation,”IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 6, pp. 10 635–10 647, 2025

    2025

  12. [12]

    Deep anomaly detection under labeling budget constraints,

    A. Li, C. Qiu, M. Kloft, P. Smyth, S. Mandt, and M. Rudolph, “Deep anomaly detection under labeling budget constraints,” inInternational Conference on Machine Learning. PMLR, 2023, pp. 19 882–19 910

    2023

  13. [13]

    Deep neural networks and tabular data: A survey,

    V . Borisov, T. Leemann, K. Seßler, J. Haug, M. Pawelczyk, and G. Kasneci, “Deep neural networks and tabular data: A survey,”IEEE transactions on neural networks and learning systems, vol. 35, no. 6, pp. 7499–7519, 2022

    2022

  14. [14]

    Why do tree-based models still outperform deep learning on typical tabular data?

    L. Grinsztajn, E. Oyallon, and G. Varoquaux, “Why do tree-based models still outperform deep learning on typical tabular data?”Advances in neural information processing systems, vol. 35, pp. 507–520, 2022

    2022

  15. [15]

    Lof: identifying density-based local outliers,

    M. M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander, “Lof: identifying density-based local outliers,” inProceedings of the 2000 ACM SIGMOD international conference on Management of data, 2000, pp. 93–104

    2000

  16. [16]

    Unsupervised anomaly detection by robust density estimation,

    B. Liu, P.-N. Tan, and J. Zhou, “Unsupervised anomaly detection by robust density estimation,” inProceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 4, 2022, pp. 4101–4108

    2022

  17. [17]

    Perturbation learning based anomaly detection,

    J. Cai and J. Fan, “Perturbation learning based anomaly detection,”Ad- vances in Neural Information Processing Systems, vol. 35, pp. 14 317– 14 330, 2022. JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2021 13

    2022

  18. [18]

    Unsupervised anomaly detection for tabular data using noise evaluation,

    W. Dai, K. Hwang, and J. Fan, “Unsupervised anomaly detection for tabular data using noise evaluation,”arXiv preprint arXiv:2412.11461, 2024

    work page arXiv 2024

  19. [19]

    Copod: copula-based outlier detection,

    Z. Li, Y . Zhao, N. Botta, C. Ionescu, and X. Hu, “Copod: copula-based outlier detection,” in2020 IEEE international conference on data mining (ICDM). IEEE, 2020, pp. 1118–1123

    2020

  20. [20]

    Ecod: Unsupervised outlier detection using empirical cumulative distribution functions,

    Z. Li, Y . Zhao, X. Hu, N. Botta, C. Ionescu, and G. H. Chen, “Ecod: Unsupervised outlier detection using empirical cumulative distribution functions,”IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 12, pp. 12 181–12 193, 2022

    2022

  21. [21]

    Isolation forest,

    F. T. Liu, K. M. Ting, and Z.-H. Zhou, “Isolation forest,” in2008 eighth ieee international conference on data mining. IEEE, 2008, pp. 413–422

    2008

  22. [22]

    Deep autoencoding gaussian mixture model for unsupervised anomaly detection,

    B. Zong, Q. Song, M. R. Min, W. Cheng, C. Lumezanu, D. Cho, and H. Chen, “Deep autoencoding gaussian mixture model for unsupervised anomaly detection,” inInternational conference on learning representa- tions, 2018

    2018

  23. [23]

    Safepredict: A meta-algorithm for machine learning that uses refusals to guarantee correctness,

    M. A. Kocak, D. Ramirez, E. Erkip, and D. E. Shasha, “Safepredict: A meta-algorithm for machine learning that uses refusals to guarantee correctness,”IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 2, pp. 663–678, 2019

    2019

  24. [24]

    Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods,

    E. H ¨ullermeier and W. Waegeman, “Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods,”Machine learning, vol. 110, no. 3, pp. 457–506, 2021

    2021

  25. [25]

    Improved Regularization of Convolutional Neural Networks with Cutout

    T. DeVries, “Improved regularization of convolutional neural networks with cutout,”arXiv preprint arXiv:1708.04552, 2017

    work page internal anchor Pith review arXiv 2017

  26. [26]

    Cutmix: Reg- ularization strategy to train strong classifiers with localizable features,

    S. Yun, D. Han, S. J. Oh, S. Chun, J. Choe, and Y . Yoo, “Cutmix: Reg- ularization strategy to train strong classifiers with localizable features,” inProceedings of the IEEE/CVF international conference on computer vision, 2019, pp. 6023–6032

    2019

  27. [27]

    A comprehensive survey of synthetic tabular data generation.arXiv preprint arXiv:2504.16506, 2025

    R. Shi, Y . Wang, M. Du, X. Shen, and X. Wang, “A compre- hensive survey of synthetic tabular data generation,”arXiv preprint arXiv:2504.16506, 2025

    work page arXiv 2025

  28. [28]

    Goggle: Generative modelling for tabular data by learning relational structure,

    T. Liu, Z. Qian, J. Berrevoets, and M. van der Schaar, “Goggle: Generative modelling for tabular data by learning relational structure,” inThe Eleventh International Conference on Learning Representations, 2023

    2023

  29. [29]

    Modeling tabular data using conditional gan,

    L. Xu, M. Skoularidou, A. Cuesta-Infante, and K. Veeramachaneni, “Modeling tabular data using conditional gan,”Advances in neural information processing systems, vol. 32, 2019

    2019

  30. [30]

    Tabddpm: Modelling tabular data with diffusion models,

    A. Kotelnikov, D. Baranchuk, I. Rubachev, and A. Babenko, “Tabddpm: Modelling tabular data with diffusion models,” inInternational confer- ence on machine learning. PMLR, 2023, pp. 17 564–17 579

    2023

  31. [31]

    Mixed-type tabular data synthesis with score-based diffusion in latent space,

    H. Zhang, J. Zhang, B. Srinivasan, Z. Shen, X. Qin, C. Faloutso, H. Rangwala, and G. Karypis, “Mixed-type tabular data synthesis with score-based diffusion in latent space,” in12th International Conference on Learning Representations, ICLR 2024, 2024

    2024

  32. [32]

    Language models are realistic tabular data generators,

    V . Borisov, K. Seßler, T. Leemann, M. Pawelczyk, and G. Kasneci, “Language models are realistic tabular data generators,” inICLR, 2023

    2023

  33. [33]

    Focal loss for dense object detection,

    T.-Y . Lin, P. Goyal, R. Girshick, K. He, and P. Doll ´ar, “Focal loss for dense object detection,” inProceedings of the IEEE international conference on computer vision, 2017, pp. 2980–2988

    2017

  34. [34]

    Adbench: Anomaly detection benchmark,

    S. Han, X. Hu, H. Huang, M. Jiang, and Y . Zhao, “Adbench: Anomaly detection benchmark,”Advances in Neural Information Processing Sys- tems, vol. 35, pp. 32 142–32 159, 2022

    2022

  35. [35]

    Drl: Decomposed representation learning for tabular anomaly detection,

    H. Ye, H. Zhao, W. Fan, M. Zhou, D. dan Guo, and Y . Chang, “Drl: Decomposed representation learning for tabular anomaly detection,” in The Thirteenth International Conference on Learning Representations, 2025

    2025

  36. [36]

    Pytorch: An imperative style, high-performance deep learning library,

    A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antigaet al., “Pytorch: An imperative style, high-performance deep learning library,”Advances in neural information processing systems, vol. 32, 2019

    2019