When Normality Shifts: Risk-Aware Test-Time Adaptation for Unsupervised Tabular Anomaly Detection

• URL: http://arxiv.org/abs/2605.10242v1
• Date: Mon, 11 May 2026 09:16:25 GMT
• Title: When Normality Shifts: Risk-Aware Test-Time Adaptation for Unsupervised Tabular Anomaly Detection
• Abstract summary: We propose a Risk-aware Test-time adaptation method for unsupervised Tabular Anomaly Detection.<n>During training, collaborative dual-task learning captures multi-level representations to establish a robust normal prior.<n>During testing, a Test-Time Contrastive Learning module explicitly accounts for adaptation risk by selectively updating the model.
• Score: 10.496488689480097
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unsupervised tabular anomaly detection methods typically learn feature patterns from normal samples during training and subsequently identify samples that deviate from these patterns as anomalies during testing. However, in practical scenarios, the limited scale and diversity of training data often lead to an incomplete characterization of normal patterns. While test-time adaptation offers a remedy, its isolated focus on test-time optimization ignores the critical synergy with training-phase learning. Furthermore, indiscriminate adaptation to unlabeled test data inevitably triggers anomaly contamination, preventing the model from fully realizing its discriminative capability between normal and anomalous samples. To address these issues, we propose RTTAD, a Risk-aware Test-time adaptation method for unsupervised Tabular Anomaly Detection. RTTAD holistically tackles normality shifts via a synergistic two-stage mechanism. During training, collaborative dual-task learning captures multi-level representations to establish a robust normal prior. During testing, a Test-Time Contrastive Learning (TTCL) module explicitly accounts for adaptation risk by selectively updating the model using high-confidence pseudo-normal samples while constraining anomalous ones. Additionally, TTCL incorporates a k-nearest neighbor-based contrastive objective to refine embedding distributions, thereby further enhancing the model's discriminative capacity. Extensive experiments on 15 tabular datasets demonstrate that RTTAD achieves state-of-the-art overall detection performance.

Related papers

• EviATTA: Evidential Active Test-Time Adaptation for Medical Segment Anything Models [26.560028123695517]
  We propose Evidential Active Test-Time Adaptation (EviATTA), which is to our knowledge, the first ATTA framework tailored for medical SAMs.<n>Specifically, we adopt the Dirichlet-based Evidential Modeling to decompose overall predictive uncertainty into distribution uncertainty and data uncertainty.<n>We show that EviATTA consistently improves adaptation reliability with minimal expert feedback under both batch-wise and instance-wise test-time adaptation settings.
  arXiv  Detail & Related papers  (2026-03-15T23:46:31Z)
• TACTIC for Navigating the Unknown: Tabular Anomaly deteCTion via In-Context inference [0.0]
  In-context learning has emerged as a new paradigm that has shifted efforts from task-specific optimization to large-scale pretraining.<n>In this paper, we study in-context models for anomaly detection and show that the unsupervised extensions to TabPFN exhibit unstable behavior.<n>We introduce TACTIC, an in-context anomaly detection approach based on pretraining with anomaly-centric synthetic priors.
  arXiv  Detail & Related papers  (2026-03-15T01:06:24Z)
• Correcting False Alarms from Unseen: Adapting Graph Anomaly Detectors at Test Time [60.341117019125214]
  We propose a lightweight and plug-and-play Test-time adaptation framework for correcting Unseen Normal pattErns in graph anomaly detection (GAD)<n>To address semantic confusion, a graph aligner is employed to align the shifted data to the original one at the graph attribute level.<n>Extensive experiments on 10 real-world datasets demonstrate that TUNE significantly enhances the generalizability of pre-trained GAD models to both synthetic and real unseen normal patterns.
  arXiv  Detail & Related papers  (2025-11-10T12:10:05Z)
• Leveraging Learning Bias for Noisy Anomaly Detection [19.23861148116995]
  This paper addresses the challenge of fully unsupervised image anomaly detection (FUIAD)<n> Conventional methods assume anomaly-free training data, but real-world contamination leads models to absorb anomalies as normal.<n>We propose a two-stage framework that exploits inherent learning bias in models.
  arXiv  Detail & Related papers  (2025-08-10T17:47:21Z)
• Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detection [109.72772150095646]
  FAPrompt is a novel framework designed to learn Fine-grained Abnormality Prompts for accurate ZSAD.<n>Experiments on 19 real-world datasets, covering both industrial defects and medical anomalies, demonstrate that FAPrompt substantially outperforms state-of-the-art methods in both image- and pixel-level ZSAD tasks.
  arXiv  Detail & Related papers  (2024-10-14T08:41:31Z)
• Generalized Robust Test-Time Adaptation in Continuous Dynamic Scenarios [18.527640606971563]
  Test-time adaptation (TTA) adapts pre-trained models to test distributions during the inference phase exclusively employing unlabeled test data streams. We propose a Generalized Robust Test-Time Adaptation (GRoTTA) method to effectively address the difficult problem.
  arXiv  Detail & Related papers  (2023-10-07T07:13:49Z)
• A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts [117.72709110877939]
  Test-time adaptation (TTA) has the potential to adapt a pre-trained model to unlabeled data during testing, before making predictions.<n>We categorize TTA into several distinct groups based on the form of test data, namely, test-time domain adaptation, test-time batch adaptation, and online test-time adaptation.
  arXiv  Detail & Related papers  (2023-03-27T16:32:21Z)
• DELTA: degradation-free fully test-time adaptation [59.74287982885375]
  We find that two unfavorable defects are concealed in the prevalent adaptation methodologies like test-time batch normalization (BN) and self-learning. First, we reveal that the normalization statistics in test-time BN are completely affected by the currently received test samples, resulting in inaccurate estimates. Second, we show that during test-time adaptation, the parameter update is biased towards some dominant classes.
  arXiv  Detail & Related papers  (2023-01-30T15:54:00Z)
• Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly Detection [81.07346419422605]
  Anomaly detection aims at identifying deviant samples from the normal data distribution. Contrastive learning has provided a successful way to sample representation that enables effective discrimination on anomalies. We propose a novel hierarchical semi-supervised contrastive learning framework, for contamination-resistant anomaly detection.
  arXiv  Detail & Related papers  (2022-07-24T18:49:26Z)
• Efficient Test-Time Model Adaptation without Forgetting [60.36499845014649]
  Test-time adaptation seeks to tackle potential distribution shifts between training and testing data. We propose an active sample selection criterion to identify reliable and non-redundant samples. We also introduce a Fisher regularizer to constrain important model parameters from drastic changes.
  arXiv  Detail & Related papers  (2022-04-06T06:39:40Z)

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.