当正常性转变时：面向无监督表格异常检测的风险感知测试时适应
When Normality Shifts: Risk-Aware Test-Time Adaptation for Unsupervised Tabular Anomaly Detection
AI总结：
本文提出RTTAD方法，通过协同双任务学习和测试时对比学习，解决表格异常检测中正常性转变问题，提升模型判别能力。
摘要：
无监督表格异常检测方法通常在训练时学习正常样本的特征模式，随后在测试时识别偏离这些模式的样本作为异常。然而，在实际场景中，训练数据的规模和多样性有限，往往导致正常模式的不完整表征。虽然测试时适应提供了解决方案，但其仅关注测试时优化，忽略了与训练阶段学习的协同作用。此外，对未标记测试数据的无差别适应不可避免地引发异常污染，阻碍模型在正常与异常样本间的判别能力的充分发挥。为了解决这些问题，我们提出RTTAD，一种面向无监督表格异常检测的风险感知测试时适应方法。RTTAD通过协同的两阶段机制全面解决正常性转变问题。在训练阶段，协同双任务学习捕获多级表示以建立稳健的正常先验。在测试阶段，测试时对比学习（TTCL）模块通过选择性更新高置信度的伪正常样本并约束异常样本，显式考虑适应风险。此外，TTCL还结合基于k最近邻的对比目标以细化嵌入分布，从而进一步增强模型的判别能力。在15个表格数据集上的广泛实验表明，RTTAD在整体检测性能上达到最先进的水平。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
