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.

翻译：近年来，异常检测方法在分布内数据上取得了显著成功。然而，现实世界数据常呈现分布偏移，导致传统异常检测方法性能大幅下降。基于此视角，先前研究鲜少探讨分布偏移下的异常检测问题，而基于反向蒸馏框架的分布不变正态性学习已被提出。但我们发现教师网络与学生网络间存在错位问题，导致检测失效，因此提出FiCo（过滤或补偿）来解决异常检测中的分布偏移问题。FiCo首先通过分布特定补偿模块补偿分布特定信息以减少师生网络间的错位，其次通过分布不变过滤模块滤除所有异常信息以捕获分布不变的正态性。在三个不同异常检测基准上的大量实验证明了FiCo的有效性，其性能超越所有现有最先进方法，甚至在分布内场景下相比基于反向蒸馏的方法取得了更优结果。我们的代码公开于https://github.com/znchen666/FiCo。