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

arXiv: https://arxiv.org/abs/2411.14509 · HTML: https://mlsanity.com/paper/2411.14509/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 37 references that are in the map.
- First posted on arXiv 21 Nov 2024.

## Builds on (most cited first, 3 of 3)

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [A multi-task learning model for malware classification with useful file access pattern from API call sequence](https://mlsanity.com/paper/1610.05945/) (2016)

## Abstract (arXiv)

We propose an End-to-end Convolutional Activation Anomaly Analysis (E2E-CA3), which is a significant extension of A3 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.