提出FiCo方法，解决异常检测中分布偏移导致的性能下降问题。
Filter or Compensate: Towards Invariant Representation from Distribution Shift for Anomaly Detection
- 通过补偿分布特异性信息减少师生网络错位
- 通过过滤异常信息捕捉不变正常模式，提升鲁棒性
- 在三个基准上超越现有方法，适用于真实场景数据
近期异常检测（AD）方法在分布内（ID）数据上取得显著成果，但真实世界数据常存在分布偏移，导致传统方法性能大幅下降。针对此问题，已有研究基于反蒸馏（RD）框架提出分布不变正常性学习，但发现师生网络间存在错位问题，影响检测效果。为此，本文提出FiCo（Filter or Compensate）方法，首先通过分布特异性补偿（DiSCo）模块补偿分布特异性信息，降低师生网络错位；其次通过分布不变滤波（DiIFi）模块过滤所有异常信息，以捕获分布不变的正常特征。在三个不同异常检测基准上的大量实验表明，FiCo优于所有现有SOTA方法，甚至在纯分布内场景下表现超过基于RD的方法。代码已开源：https://github.com/znchen666/FiCo。
原文摘要 · Abstract (English)
Recent Anomaly Detection (AD) methods have achieved great success with In-Distribution (ID) data. However, real-world data often exhibits distribution shift, causing huge performance decay on traditional AD methods. From this perspective, few previous work has explored AD with distribution shift, and the distribution-invariant normality learning has been proposed based on the Reverse Distillation (RD) framework. However, we observe the misalignment issue between the teacher and the student network that causes detection failure, thereby propose FiCo, Filter or Compensate, to address the distribution shift issue in AD. FiCo firstly compensates the distribution-specific information to reduce the misalignment between the teacher and student network via the Distribution-Specific Compensation (DiSCo) module, and secondly filters all abnormal information to capture distribution-invariant normality with the Distribution-Invariant Filter (DiIFi) module. Extensive experiments on three different AD benchmarks demonstrate the effectiveness of FiCo, which outperforms all existing state-of-the-art (SOTA) methods, and even achieves better results on the ID scenario compared with RD-based methods. Our code is available at https://github.com/znchen666/FiCo.
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