Double-Adversarial Activation Anomaly Detection: Adversarial Autoencoders are Anomaly Generators

@article{Schulze2021DoubleAdversarialAA,
  title={Double-Adversarial Activation Anomaly Detection: Adversarial Autoencoders are Anomaly Generators},
  author={Jan-Philipp Schulze and Philip Sperl and Konstantin B{\"o}ttinger},
  journal={2022 International Joint Conference on Neural Networks (IJCNN)},
  year={2021},
  pages={1-8}
}
Anomaly detection is a challenging task for machine learning methods due to the inherent class imbalance. It is costly and time-demanding to manually analyse the observed data, thus usually only few known anomalies if any are available. Inspired by generative models and the analysis of the hidden activations of neural networks, we introduce a novel unsupervised anomaly detection method called DA3D. Here, we use adversarial autoencoders to generate anomalous counterexamples based on the normal… 
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