Anomaly detection in time series analysis is a pivotal task, yet it poses the challenge of discerning normal and abnormal patterns in label-deficient scenarios. While prior studies have largely employed reconstruction-based approaches, which limits the models' representational capacities. Moreover, existing deep learning-based methods are not sufficiently lightweight. Addressing these issues, we present PatchAD, our novel, highly efficient multiscale patch-based MLP-Mixer architecture that utilizes contrastive learning for representation extraction and anomaly detection. With its four distinct MLP Mixers and innovative dual project constraint module, PatchAD mitigates potential model degradation and offers a lightweight solution, requiring only $3.2$MB. Its efficacy is demonstrated by state-of-the-art results across $9$ datasets sourced from different application scenarios, outperforming over $30$ comparative algorithms. PatchAD significantly improves the classical F1 score by $50.5\%$, the Aff-F1 score by $7.8\%$, and the AUC by $10.0\%$. The code is publicly available. \url{https://github.com/EmorZz1G/PatchAD}

翻译：[translated abstract in Chinese] 时间序列分析中的异常检测是一项关键任务，但在标签稀缺场景下区分正常与异常模式仍具挑战性。现有研究多采用基于重构的方法，这限制了模型的表示能力。此外，现有基于深度学习的方法在轻量性方面存在不足。针对这些问题，我们提出了PatchAD——一种新颖、高效的多尺度分块MLP-Mixer架构，通过对比学习实现表示提取与异常检测。该架构包含四个独立的MLP Mixer模块与创新的双投影约束模块，可有效缓解模型退化问题，并提供仅需3.2MB的轻量级解决方案。它在来自不同应用场景的9个数据集上取得了当前最优结果，性能超越30余种对比算法。PatchAD将经典F1分数显著提升50.5%，Aff-F1分数提升7.8%，AUC提升10.0%。相关代码已开源发布。\url{https://github.com/EmorZz1G/PatchAD}