Anomaly detection stands as a crucial aspect of time series analysis, aiming
to identify abnormal events in time series samples. The central challenge of
this task lies in effectively learning the representations of normal and
abnormal patterns in a label-lacking scenario. Previous research mostly relied
on reconstruction-based approaches, restricting the representational abilities
of the models. In addition, most of the current deep learning-based methods are
not lightweight enough, which prompts us to design a more efficient framework
for anomaly detection. In this study, we introduce PatchAD, a novel multi-scale
patch-based MLP-Mixer architecture that leverages contrastive learning for
representational extraction and anomaly detection. Specifically, PatchAD is
composed of four distinct MLP Mixers, exclusively utilizing the MLP
architecture for high efficiency and lightweight architecture. Additionally, we
also innovatively crafted a dual project constraint module to mitigate
potential model degradation. Comprehensive experiments demonstrate that PatchAD
achieves state-of-the-art results across multiple real-world multivariate time
series datasets. Our code is publicly
available.\footnote{https://github.com/EmorZz1G/PatchAD}

PatchAD 是一种新颖的多尺度基于补丁的 MLP-Mixer 架构，利用对比学习进行表示提取和异常检测的方法，通过使用 MLP 架构，高效轻量化地实现了一种先进的异常检测方案。