Unsupervised anomaly detection (UAD) is a widely adopted approach in industry due to rare anomaly occurrences and data imbalance. A desirable characteristic of an UAD model is contained generalization ability which excels in the reconstruction of seen normal patterns but struggles with unseen anomalies. Recent studies have pursued to contain the generalization capability of their UAD models in reconstruction from different perspectives, such as design of neural network (NN) structure and training strategy. In contrast, we note that containing of generalization ability in reconstruction can also be obtained simply from steep-shaped loss landscape. Motivated by this, we propose a loss landscape sharpening method by amplifying the reconstruction loss, dubbed Loss AMPlification (LAMP). LAMP deforms the loss landscape into a steep shape so the reconstruction error on unseen anomalies becomes greater. Accordingly, the anomaly detection performance is improved without any change of the NN architecture. Our findings suggest that LAMP can be easily applied to any reconstruction error metrics in UAD settings where the reconstruction model is trained with anomaly-free samples only.

翻译：无监督异常检测（UAD）因异常样本稀少及数据不平衡问题，在工业领域得到广泛应用。此类模型的核心特性在于具备可控泛化能力：既能精准重构可见的正常模式，又能对未见异常产生重构失效。近期研究从神经网络结构设计与训练策略等不同角度，致力于抑制UAD模型在重构过程中的泛化能力。与此不同，我们注意到通过陡峭的损失曲面即可实现重构泛化能力的抑制。受此启发，我们提出一种通过放大重构损失来锐化损失曲面的方法——损失放大策略（LAMP）。LAMP通过将损失曲面改造为陡峭形态，使得未见异常对应的重构误差显著增大。该方法无需改变网络架构即可提升异常检测性能。实验表明，在仅使用无异常样本训练重构模型的UAD场景中，LAMP可便捷地适配各类重构误差度量指标。