基于证据的后验调整框架用于数据污染下的异常检测
An Evidence-Based Post-Hoc Adjustment Framework for Anomaly Detection Under Data Contamination
AI总结：
EPHAD提出一种基于证据的后验调整框架，通过测试时收集的证据提升异常检测在数据污染下的性能。
摘要：
无监督异常检测（AD）方法通常假设训练数据干净，但现实世界的数据集往往包含未检测或误标记的异常，导致性能显著下降。现有解决方案需要访问训练管道、数据或对数据中异常比例的先验知识，限制了其在现实中的应用。为了解决这一挑战，我们提出了EPHAD，一种简单而有效的测试时适应框架，通过在测试时收集的证据更新在污染数据集上训练的AD模型的输出。我们的方法整合了由在污染数据集上训练的AD模型捕获的先验知识，以及来自多模态基础模型如对比语言-图像预训练（CLIP）、经典AD方法如局部异常因子或领域特定知识的证据。我们通过一个合成玩具示例说明EPHAD的直觉，并通过涵盖八个视觉AD数据集、二十六个表格AD数据集和一个现实工业AD数据集的全面实验验证其有效性。此外，我们进行了消融研究以分析超参数影响和对不同污染水平的鲁棒性，证明了EPHAD在各种AD模型和证据对中的通用性和鲁棒性。为确保可重复性，我们的代码在https://github.com/sukanyapatra1997/EPHAD上公开。
英文摘要：
Unsupervised anomaly detection (AD) methods typically assume clean training data, yet real-world datasets often contain undetected or mislabeled anomalies, leading to significant performance degradation. Existing solutions require access to the training pipelines, data or prior knowledge of the proportions of anomalies in the data, limiting their real-world applicability. To address this challenge, we propose EPHAD, a simple yet effective test-time adaptation framework that updates the outputs of AD models trained on contaminated datasets using evidence gathered at test time. Our approach integrates the prior knowledge captured by the AD model trained on contaminated datasets with evidence derived from multimodal foundation models like Contrastive Language-Image Pre-training (CLIP), classical AD methods like the Local Outlier Factor or domain-specific knowledge. We illustrate the intuition behind EPHAD using a synthetic toy example and validate its effectiveness through comprehensive experiments across eight visual AD datasets, twenty-six tabular AD datasets, and a real-world industrial AD dataset. Additionally, we conduct an ablation study to analyse hyperparameter influence and robustness to varying contamination levels, demonstrating the versatility and robustness of EPHAD across diverse AD models and evidence pairs. To ensure reproducibility, our code is publicly available at https://github.com/sukanyapatra1997/EPHAD.
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。
