用于异常检测的端到端卷积激活异常分析
End-to-End Convolutional Activation Anomaly Analysis for Anomaly Detection
AI总结：
本文提出端到端卷积激活异常分析方法E2E-CA$^3$，通过卷积自编码器与CNN报警网络结合分类损失和重构误差，在图像和表格数据上实现高效异常检测。
摘要：
我们提出了一种端到端卷积激活异常分析（E2E-CA$^3$），这是对Sperl、Schulze和Böttinger提出的A$^3$异常检测方法在架构和应用范围上的重大扩展。与原始思路不同，我们采用卷积自编码器作为目标网络，这使得该方法能够自然地应用于图像和表格数据。报警网络同样设计为CNN，其中来自CAE的卷积层激活被堆叠成一个$k+1$维张量。此外，我们将报警网络的分类损失与目标CAE的重构误差相结合，作为一种“两全其美”的方法，极大地提高了网络的通用性。评估表明，尽管架构总体简单且轻量，它在MNIST、CIFAR-10和KDDcup99等常见数据集上表现出非常有前景的异常检测性能。
英文摘要：
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.
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。
