Browse > Methodology > Anomaly Detection > Unsupervised Anomaly Detection

Unsupervised Anomaly Detection

8 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

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

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

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

Band selection with Higher Order Multivariate Cumulants for small target detection in hyperspectral images

10 Aug 2018ZKSI/CumFSel.jl

In this paper we present an analysis of a general algorithm for band selection based on higher order cumulants.

UNSUPERVISED ANOMALY DETECTION

Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images

12 Apr 2018bumuckl/AutoencodersForUnsupervisedAnomalyDetection

Reliably modeling normality and differentiating abnormal appearances from normal cases is a very appealing approach for detecting pathologies in medical images.

REPRESENTATION LEARNING UNSUPERVISED ANOMALY DETECTION

How to Evaluate the Quality of Unsupervised Anomaly Detection Algorithms?

5 Jul 2016bstienen/unsupervised-learning-metrics

When sufficient labeled data are available, classical criteria based on Receiver Operating Characteristic (ROC) or Precision-Recall (PR) curves can be used to compare the performance of un-supervised anomaly detection algorithms.

UNSUPERVISED ANOMALY DETECTION