Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection

By Bo Zong, Qi Song, Martin Renqiang Min, Wei Cheng, Cristian Lumezanu, Dae-ki Cho, Haifeng Chen. International Conference on Learning Representations, 2018

2,127 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/dbc7401e3e75c40d3c720e7db3c906d48bd742d7

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Citations in the corpus

Adversarially Learned Anomaly Detection (2018, methodology): “Deep Autoencoding Gaussian Mixture Model (DAGMM) [23] is a state-of-the-art autoencoder-based method for anomaly detection.”
An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series (2021, methodology): “Model DMDS MSL SKAB SMAP SMD SWaT WADI Mean Avg Rank Raw Signal 0.7073 0.4379 0.4855 0.4953 0.7475 0.7273 0.7733 0.6249 10.0 PCA 0.7413 0.6175 0.5037 0.5684 0.7900 0.8249 0.6583 0.6720 7.3 UAE 0.8852 0.6712 0.5088 0.6256 0.8298 0.8279 0.7405 0.7270 2.6 FC AE 0.9280 0.6602 0.4914 0.6272 0.8028 0.8085”
Graph Neural Network-Based Anomaly Detection in Multivariate Time Series (2021, methodology): “• DAGMM: Deep Autoencoding Gaussian Model joints deep Autoencoders and Gaussian Mixture Model to generate a low-dimensional representation and reconstruction error for each observation (Zong et al. 2018).”
Classification-Reconstruction Learning for Open-Set Recognition (2018, methodology): “The deep approaches mainly use autoencoders trained in an unsupervised manner [51], in combination with GMM [53], clustering [1], or one-class learning [29].”
Exploring Interpretable LSTM Neural Networks over Multi-Variable Data (2019, background): “These two steps are assembled into a probabilistic mixture model (Zong et al., 2018; Graves, 2013; Bishop, 1994), which facilitates the subsequent learning, predicting, and interpreting.”
Is Out-of-Distribution Detection Learnable? (2022, methodology): “A rich line of algorithms have been developed to empirically address the OOD detection problem [6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20].”
CutPaste: Self-Supervised Learning for Anomaly Detection and Localization (2021, background): “Anomaly detection under one-class classification setting, where we assume only the normal data is given during the training, has been widely studied [49, 56, 46, 63, 66, 13, 42, 36, 27].”
Deep Anomaly Detection Using Geometric Transformations (2018, methodology): “[38] uses the representation layer of an autoencoder in order to estimate parameters of a Gaussian mixture model.”
CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances (2020, background): “Most recent works can be grouped into four categories: (a) density-based [66, 43, 7, 12, 52, 57, 17], (b) reconstruction-based [55, 68, 10, 50, 48, 8], (c) one-class classiﬁer [56, 53], and (d) self-supervised [15, 26, 1] methods.”
Progress in Outlier Detection Techniques: A Survey (2019, background): “In most of unsupervised deep OD models, autoencoders play a central role [210], [238].”
PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation (2020, methodology): “Novel deep methods have also been proposed for anomaly detection including GAN-based methods [28].”
TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data (2022, methodology): “DAGMM model performs very well for short datasets like UCR, NAB, MBA and SMAP, but its scores drop significantly for other datasets with longer sequences.”
MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks (2019, methodology): “. For instance, the Auto-Encoder (AE) [21] is a popular deep learning model for anomaly detection by inspecting its reconstruction errors. Others like Deep Autoencoding Gaussian Mixture Model (DAGMM) [22] and LSTM Encoder-Decoder [23] have also reported good performance for multivariate anomaly dete”
A Comprehensive Survey on Graph Anomaly Detection With Deep Learning (2021, background): “com/minjiyoon/anomrank DAGMM [130] Python Pytorch Dynamic Graph https://github.”
Student-Teacher Feature Pyramid Matching for Anomaly Detection (2021, methodology): “Recent algorithms such as anomaly detection GAN (ADGAN) [12] and deep autoencoding Gaussian mixture model (DAGMM) [48] learn a deep projection that maps high-dimensional images into a low-dimensional latent space.”