… Deep autoencoding Gaussian mixture model (DAGMM) employs dimensionality reduction
and density estimation and jointly optimizes them for unsupervised anomaly detection tasks. …

… To deal with outliers mixed in chiller data, this paper … deep autoencoding Gaussian
mixture model (S-DAGMM) algorithm which is an ensemble model of individual unsupervised …

… The deep autoencoder is a popular deep learning model that is constructed with … an
unsupervised ensemble autoencoder Gaussian mixture model for cyberattack anomaly detection. It …

… a deep autoencoder (DA) or Gaussian mixture model (GMM), have poor anomaly-detection …
new method based on a deep autoencoding Gaussian mixture model with hyper-parameter …

… Unsupervised anomaly detection deals with traffic data that are manually extracted from …
a deep autoencoding Gaussian mixture model (DAGMM) which uses a deep autoencoder to …

… We studied the application of the Deep Autoencoding Gaussian Mixture Model unsupervised
learning algorithm to anomaly detection in the UNSW-NB15 dataset. It is found that the …

… We train an autoencoder from the normal data subset, and iterate between hypothesizing …
network based on Gaussian Mixture Model(GMM). However, as its autoencoder was trained on …

… with an unsupervised machine learning model to preserve the input space topology using
the self-organizing map that is handled by the deep autoencoder for anomaly detection in IoT …

… combine autoencoder and Gaussian mixture model … Deep SVDD regarding anomaly
detection performance by all metrics. Since our method mainly depends on autoencoder and Deep …

… a new unsupervised anomaly detection method, MemAe-gmm-ma. The model uses a deep
… (2019) Memorizing normality to detect anomaly: memory-augmented deep autoencoder for …