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Unsupervised Anomaly Detection

14 papers with code · Methodology
Subtask of Anomaly Detection

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Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications

12 Feb 2018korepwx/donut

To ensure undisrupted business, large Internet companies need to closely monitor various KPIs (e. g., Page Views, number of online users, and number of orders) of its Web applications, to accurately detect anomalies and trigger timely troubleshooting/mitigation.

UNSUPERVISED ANOMALY DETECTION

Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery

17 Mar 2017LeeDoYup/AnoGAN

Obtaining models that capture imaging markers relevant for disease progression and treatment monitoring is challenging.

UNSUPERVISED ANOMALY DETECTION

Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection

ICLR 2018 danieltan07/dagmm

In this paper, we present a Deep Autoencoding Gaussian Mixture Model (DAGMM) for unsupervised anomaly detection.

DENSITY ESTIMATION DIMENSIONALITY REDUCTION UNSUPERVISED ANOMALY DETECTION

Unsupervised Detection of Anomalous Sound based on Deep Learning and the Neyman-Pearson Lemma

22 Oct 2018lifesailor/data-driven-predictive-maintenance

To calculate the TPR in the objective function, we consider that the set of anomalous sounds is the complementary set of normal sounds and simulate anomalous sounds by using a rejection sampling algorithm.

UNSUPERVISED ANOMALY DETECTION UNSUPERVISED ANOMALY DETECTION IN SOUND

Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection

4 Apr 2019donggong1/memae-anomaly-detection

At the test stage, the learned memory will be fixed, and the reconstruction is obtained from a few selected memory records of the normal data.

UNSUPERVISED ANOMALY DETECTION

Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection

25 Jan 2019samet-akcay/skip-ganomaly

By contrast, we introduce an unsupervised anomaly detection model, trained only on the normal (non-anomalous, plentiful) samples in order to learn the normality distribution of the domain and hence detect abnormality based on deviation from this model.

SCENE UNDERSTANDING UNSUPERVISED ANOMALY DETECTION

DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GAN

23 Aug 2018greentfrapp/doping

To the best of our knowledge, our method is the first data augmentation technique focused on improving performance in unsupervised anomaly detection.

DATA AUGMENTATION UNSUPERVISED ANOMALY DETECTION

Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier features

13 Apr 2018minh-nghia/AE-1SVM

To the best of our knowledge, this is the first work to study the interpretability of deep learning in anomaly detection.

DECISION MAKING DIMENSIONALITY REDUCTION REPRESENTATION LEARNING UNSUPERVISED ANOMALY DETECTION

Learning Neural Representations for Network Anomaly Detection

IEEE Transactions on Cybernetics 2019 vanloicao/SAEDVAE

Our approach is to introduce new regularizers to a classical autoencoder (AE) and a variational AE, which force normal data into a very tight area centered at the origin in the nonsaturating area of the bottleneck unit activations.

INTRUSION DETECTION MODEL SELECTION UNSUPERVISED ANOMALY DETECTION

A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data

20 Nov 2018KONI-SZ/MSCRED

Subsequently, given the signature matrices, a convolutional encoder is employed to encode the inter-sensor (time series) correlations and an attention based Convolutional Long-Short Term Memory (ConvLSTM) network is developed to capture the temporal patterns.

TIME SERIES UNSUPERVISED ANOMALY DETECTION