缺陷表示的特征衰减可解决异常检测中的不完整掩码问题
Feature Attenuation of Defective Representation Can Resolve Incomplete Masking on Anomaly Detection
AI总结：
本文提出FADeR方法，仅用两个MLP层在解码时衰减异常特征，以解决异常检测中掩码无法完全覆盖异常区域的问题，实现高效可扩展的性能提升。
摘要：
在无监督异常检测（UAD）研究中，虽然最先进的模型在公共基准数据集上的广泛研究已达到饱和点，但它们采用大规模定制神经网络（NN）来提升检测性能，或追求面向多种任务的统一模型。面向边缘计算，有必要开发一种计算高效且可扩展的解决方案，避免大规模复杂神经网络。受此启发，我们旨在以最小的神经网络设置改动来优化UAD性能。因此，我们重新审视基于修复的重建方法，并通过分析其优缺点来改进它。SOTA方法的优势在于采用单一确定性掩码方式，解决了随机多重掩码带来的推理延迟和输出不一致问题。然而，无法提供完全覆盖异常区域的掩码仍然是一个弱点。为缓解这一问题，我们提出了缺陷表示的特征衰减（FADeR），仅使用两个MLP层，在解码过程中衰减异常重建的特征信息。通过利用FADeR，未见异常模式的特征被重建为已见的正常模式，从而减少误报。实验结果表明，与相似规模的神经网络相比，FADeR实现了性能提升。此外，我们的方法在以即插即用方式与其他单一确定性掩码方法集成时，表现出性能增强的可扩展性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
