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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2411.14509 |
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| _version_ | 1866917844717928448 |
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| author | Kozłowski, Aleksander Ponikowski, Daniel Żukiewicz, Piotr Twardowski, Paweł |
| author_facet | Kozłowski, Aleksander Ponikowski, Daniel Żukiewicz, Piotr Twardowski, Paweł |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_14509 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | End-to-End Convolutional Activation Anomaly Analysis for Anomaly Detection Kozłowski, Aleksander Ponikowski, Daniel Żukiewicz, Piotr Twardowski, Paweł Machine Learning 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. |
| title | End-to-End Convolutional Activation Anomaly Analysis for Anomaly Detection |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2411.14509 |