用卷积网络统一处理图像与表格数据的异常检测方法
End-to-End Convolutional Activation Anomaly Analysis for Anomaly Detection
- 用卷积自编码器+卷积警报网络端到端建模
- 在MNIST、CIFAR-10等数据集上表现优异
- 轻量结构适合工业部署，支持多类型数据
我们提出一种端到端卷积激活异常分析（E2E-CA³），是对Sperl、Schulze和Böttinger提出的A³异常检测方法的重要扩展，涵盖架构与应用范围。与原方法不同，我们采用卷积自编码器（CAE）作为目标网络，使该方法可自然应用于图像和表格数据。警报网络也设计为卷积神经网络（CNN），将CAE各卷积层的激活值堆叠成k+1维张量。此外，我们将警报网络的分类损失与目标CAE的重建误差结合，实现‘双优融合’，显著提升模型通用性。评估表明，尽管架构简单轻量，其在常见数据集如MNIST、CIFAR-10和KDDcup99上均展现出出色的异常检测性能。
原文摘要 · Abstract (English)
We propose an End-to-end Convolutional Activation Anomaly Analysis (E2E-CA$^3$), which is a significant extension of A$^3$ anomaly detection approach proposed by Sperl, Schulze and Böttinger, both in terms of architecture and scope of application. In contrast to the original idea, we utilize a convolutional autoencoder as a target network, which allows for natural application of the method both to image and tabular data. The alarm network is also designed as a CNN, where the activations of convolutional layers from CAE are stacked together into $k+1-$dimensional tensor. Moreover, we combine the classification loss of the alarm network with the reconstruction error of the target CAE, as a "best of both worlds" approach, which greatly increases the versatility of the network. The evaluation shows that despite generally straightforward and lightweight architecture, it has a very promising anomaly detection performance on common datasets such as MNIST, CIFAR-10 and KDDcup99.
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