End-to-End Convolutional Activation Anomaly Analysis for Anomaly Detection

• URL: http://arxiv.org/abs/2411.14509v1
• Date: Thu, 21 Nov 2024 10:22:50 GMT
• Title: End-to-End Convolutional Activation Anomaly Analysis for Anomaly Detection
• Authors: Aleksander Kozłowski, Daniel Ponikowski, Piotr Żukiewicz, Paweł Twardowski,
• Abstract summary: End-to-end Convolutional Activation Anomaly Analysis (E2E-CA$3$) We propose an End-to-end Convolutional Activation Anomaly Analysis (E2E-CA$3$) It is a significant extension of A$3$ anomaly detection approach proposed by Sperl, Schulze and B"ottinger.
• Score: 41.94295877935867
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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 B\"ottinger, 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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