Anomaly detection aims to recognize samples with anomalous and unusual patterns with respect to a set of normal data. This is significant for numerous domain applications, such as industrial inspection, medical imaging, and security enforcement. There are two key research challenges associated with existing anomaly detection approaches: (1) many approaches perform well on low-dimensional problems however the performance on high-dimensional instances, such as images, is limited; (2) many approaches often rely on traditional supervised approaches and manual engineering of features, while the topic has not been fully explored yet using modern deep learning approaches, even when the well-label samples are limited. In this paper, we propose a One-for-all Image Anomaly Detection system (OIAD) based on disentangled learning using only clean samples. Our key insight is that the impact of small perturbation on the latent representation can be bounded for normal samples while anomaly images are usually outside such bounded intervals, referred to as structure consistency. We implement this idea and evaluate its performance for anomaly detection. Our experiments with three datasets show that OIAD can detect over $90\%$ of anomalies while maintaining a low false alarm rate. It can also detect suspicious samples from samples labeled as clean, coincided with what humans would deem unusual.

中文翻译：

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OIAD：具有解缠结学习的一对多图像异常检测

异常检测旨在识别相对于一组正常数据具有异常和异常模式的样本。这对于众多领域应用来说意义重大，例如工业检测、医学成像和安全执法。现有的异常检测方法存在两个关键的研究挑战：（1）许多方法在低维问题上表现良好，但在高维实例（如图像）上的表现有限；(2) 许多方法通常依赖于传统的监督方法和特征的手动工程，而使用现代深度学习方法尚未充分探索该主题，即使在井标签样本有限的情况下。在本文中，我们提出了一种基于仅使用干净样本的分离学习的一体式图像异常检测系统（OIAD）。我们的主要见解是，对于正常样本，小扰动对潜在表示的影响是有界的，而异常图像通常在这种有界区间之外，称为结构一致性。我们实现了这个想法并评估了它的异常检测性能。我们对三个数据集的实验表明，OIAD 可以检测到超过 90%$ 的异常，同时保持较低的误报率。它还可以从标记为干净的样本中检测可疑样本，这与人类认为不寻常的样本一致。我们对三个数据集的实验表明，OIAD 可以检测到超过 90%$ 的异常，同时保持较低的误报率。它还可以从标记为干净的样本中检测可疑样本，这与人类认为不寻常的样本一致。我们对三个数据集的实验表明，OIAD 可以检测到超过 90%$ 的异常，同时保持较低的误报率。它还可以从标记为干净的样本中检测可疑样本，这与人类认为不寻常的样本一致。