SIM: Subspace Interaction-based Method for Token-Level Text Anomaly Detection

• URL: http://arxiv.org/abs/2609.08200v1
• Date: Tue, 08 Sep 2026 03:38:42 GMT
• Title: SIM: Subspace Interaction-based Method for Token-Level Text Anomaly Detection
• Abstract summary: Token-level text anomaly detection plays a critical role in various real-world applications, such as spam filtering and fake news detection.<n>We propose a Subspace Interaction-based Method (SIM) for token-level text anomaly detection.
• Score: 13.685698284457898
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Token-level text anomaly detection, as an emerging trend of text anomaly detection, moves beyond coarse-grained document-level detection by localizing anomalous tokens within text. By providing fine-grained abnormality prediction, token-level text anomaly detection plays a critical role in various real-world applications, such as spam filtering and fake news detection. However, existing methods still rely on the global distance calculation for scoring, during which the local anomaly signals are severely diluted by numerous redundant normal feature dimensions. Moreover, pre-trained language models used in these methods inevitably smooth out surface anomalies, further limiting their effectiveness in token-level anomaly detection. To address these limitations, we propose a Subspace Interaction-based Method (SIM for short) for token-level text anomaly detection. To prevent local signal dilution, SIM adopts a subspace interaction-based anomaly detector, which decouples high-dimensional token embeddings into multiple low-dimensional ones, amplifying localized anomaly signals hidden within specific dimensions. To counteract the over-smoothing effect, we design a hard pseudo-anomaly generation module to construct pseudo-anomalous tokens, simulating the subtle anomalies obscured by semantic smoothing. Also, a probabilistic boundary loss is developed to standardize anomaly scores into statistical distances, effectively enforcing anomalous instances to deviate significantly from the normal distribution center. Extensive experiments on multiple benchmark datasets verify the effectiveness of SIM and demonstrate its remarkable efficiency, robustness, and interpretability. The source code is available at: https://github.com/yankehan/SIM-TAD.

Related papers

• Enhancing Tabular Anomaly Detection via Pseudo-Label-Guided Generation [35.2283498099342]
  We propose PLAG, a pseudo-label-guided anomaly generation method.<n>We show PLAG achieves state-of-the-art performance against eight representative baselines.<n>As a flexible framework, PLAG integrates seamlessly with existing unsupervised detectors.
  arXiv  Detail & Related papers  (2026-04-20T13:42:30Z)
• Multi-Cue Anomaly Detection and Localization under Data Contamination [0.6703429330486276]
  We propose a robust anomaly detection framework that integrates limited anomaly supervision into the adaptive deviation learning paradigm.<n>Our framework achieves strong detection and localization performance, interpretability, and robustness under various levels of data contamination.
  arXiv  Detail & Related papers  (2026-01-30T12:34:13Z)
• Towards Token-Level Text Anomaly Detection [48.821180044375176]
  We introduce token-level anomaly detection, a novel paradigm that enables fine-grained localization of anomalies within text.<n>We formally define text anomalies at both document and token-levels, and propose a unified detection framework that operates across multiple levels.
  arXiv  Detail & Related papers  (2026-01-20T06:27:09Z)
• Adaptive Signal Analysis for Automated Subsurface Defect Detection Using Impact Echo in Concrete Slabs [0.0]
  This pilot study presents a novel, automated, and scalable methodology for detecting subsurface defect-prone regions in concrete slabs.<n>The approach integrates advanced signal processing, clustering, and visual analytics to identify subsurface anomalies.<n>The results demonstrate the robustness of the methodology, consistently identifying defect-prone areas with minimal false positives and few missed defects.
  arXiv  Detail & Related papers  (2024-12-23T20:05:53Z)
• Anomaly Detection of Tabular Data Using LLMs [54.470648484612866]
  We show that pre-trained large language models (LLMs) are zero-shot batch-level anomaly detectors. We propose an end-to-end fine-tuning strategy to bring out the potential of LLMs in detecting real anomalies.
  arXiv  Detail & Related papers  (2024-06-24T04:17:03Z)
• AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model [59.08735812631131]
  Anomaly inspection plays an important role in industrial manufacture. Existing anomaly inspection methods are limited in their performance due to insufficient anomaly data. We propose AnomalyDiffusion, a novel diffusion-based few-shot anomaly generation model.
  arXiv  Detail & Related papers  (2023-12-10T05:13:40Z)
• MSFlow: Multi-Scale Flow-based Framework for Unsupervised Anomaly Detection [124.52227588930543]
  Unsupervised anomaly detection (UAD) attracts a lot of research interest and drives widespread applications. An inconspicuous yet powerful statistics model, the normalizing flows, is appropriate for anomaly detection and localization in an unsupervised fashion. We propose a novel Multi-Scale Flow-based framework dubbed MSFlow composed of asymmetrical parallel flows followed by a fusion flow. Our MSFlow achieves a new state-of-the-art with a detection AUORC score of up to 99.7%, localization AUCROC score of 98.8%, and PRO score of 97.1%.
  arXiv  Detail & Related papers  (2023-08-29T13:38:35Z)
• Hard-normal Example-aware Template Mutual Matching for Industrial Anomaly Detection [78.734927709231]
  Anomaly detectors are widely used in industrial manufacturing to detect and localize unknown defects in query images.<n>These detectors are trained on anomaly-free samples and have successfully distinguished anomalies from most normal samples.<n>However, hard-normal examples are scattered and far apart from most normal samples, and thus they are often mistaken for anomalies by existing methods.
  arXiv  Detail & Related papers  (2023-03-28T17:54:56Z)
• Explainable Deep Few-shot Anomaly Detection with Deviation Networks [123.46611927225963]
  We introduce a novel weakly-supervised anomaly detection framework to train detection models. The proposed approach learns discriminative normality by leveraging the labeled anomalies and a prior probability. Our model is substantially more sample-efficient and robust, and performs significantly better than state-of-the-art competing methods in both closed-set and open-set settings.
  arXiv  Detail & Related papers  (2021-08-01T14:33:17Z)
• Unsupervised Learning of Multi-level Structures for Anomaly Detection [16.037822355038443]
  This paper introduces a novel method to generate anomalous data by breaking up global structures. It can efficiently expose local abnormal structures of various levels. By aggregating the outputs of all level-specific detectors, we obtain a model that can detect all potential anomalies.
  arXiv  Detail & Related papers  (2021-04-25T08:38:41Z)
• Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly Data [150.9270911031327]
  We consider the problem of anomaly detection with a small set of partially labeled anomaly examples and a large-scale unlabeled dataset. Existing related methods either exclusively fit the limited anomaly examples that typically do not span the entire set of anomalies, or proceed with unsupervised learning from the unlabeled data. We propose here instead a deep reinforcement learning-based approach that enables an end-to-end optimization of the detection of both labeled and unlabeled anomalies.
  arXiv  Detail & Related papers  (2020-09-15T03:05:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.