AMAD is an end-to-end model using adversarial autoencoders and RNNs with attention for multiscale anomaly detection on time-evolving high-dimensional categorical data.
In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
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AMAD: Adversarial Multiscale Anomaly Detection on High-Dimensional and Time-Evolving Categorical Data
AMAD is an end-to-end model using adversarial autoencoders and RNNs with attention for multiscale anomaly detection on time-evolving high-dimensional categorical data.