In unsupervised anomaly detection (UAD) research, while state-of-the-art models have reached a saturation point with extensive studies on public benchmark datasets, they adopt large-scale tailor-made neural networks (NN) for detection performance or pursued unified models for various tasks. Towards edge computing, it is necessary to develop a computationally efficient and scalable solution that avoids large-scale complex NNs. Motivated by this, we aim to optimize the UAD performance with minimal changes to NN settings. Thus, we revisit the reconstruction-by-inpainting approach and rethink to improve it by analyzing strengths and weaknesses. The strength of the SOTA methods is a single deterministic masking approach that addresses the challenges of random multiple masking that is inference latency and output inconsistency. Nevertheless, the issue of failure to provide a mask to completely cover anomalous regions is a remaining weakness. To mitigate this issue, we propose Feature Attenuation of Defective Representation (FADeR) that only employs two MLP layers which attenuates feature information of anomaly reconstruction during decoding. By leveraging FADeR, features of unseen anomaly patterns are reconstructed into seen normal patterns, reducing false alarms. Experimental results demonstrate that FADeR achieves enhanced performance compared to similar-scale NNs. Furthermore, our approach exhibits scalability in performance enhancement when integrated with other single deterministic masking methods in a plug-and-play manner.

翻译：在无监督异常检测（UAD）研究中，尽管现有先进模型在公开基准数据集上的研究已趋于饱和，并采用大规模定制神经网络（NN）以提升检测性能，或追求适用于多种任务的统一模型，但面向边缘计算场景，亟需开发一种计算高效且可扩展的解决方案，避免使用大规模复杂神经网络。受此驱动，本研究旨在以最小化神经网络配置改动为前提优化UAD性能。为此，我们重新审视基于修复的重建方法，通过系统分析其优势与不足以寻求改进路径。现有先进方法的优势在于采用单一确定性掩码策略，有效解决了随机多重掩码导致的推理延迟与输出不一致问题。然而，掩码无法完全覆盖异常区域仍是亟待解决的缺陷。为缓解此问题，我们提出缺陷表示的特征衰减方法（FADeR），该方法仅采用两个MLP层，在解码阶段衰减异常重建的特征信息。通过FADeR机制，未见异常模式的特征可被重建为已见正常模式，从而降低误报率。实验结果表明，与同等规模的神经网络相比，FADeR实现了性能提升。此外，本方法以即插即用方式与其他单一确定性掩码方法集成时，展现出性能增强的可扩展性。