  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}
