SOTAVerified

Unsupervised Anomaly Detection

The objective of Unsupervised Anomaly Detection is to detect previously unseen rare objects or events without any prior knowledge about these. The only information available is that the percentage of anomalies in the dataset is small, usually less than 1%. Since anomalies are rare and unknown to the user at training time, anomaly detection in most cases boils down to the problem of modelling the normal data distribution and defining a measurement in this space in order to classify samples as anomalous or normal. In high-dimensional data such as images, distances in the original space quickly lose descriptive power (curse of dimensionality) and a mapping to some more suitable space is required.

Source: Unsupervised Learning of Anomaly Detection from Contaminated Image Data using Simultaneous Encoder Training

Papers

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Showing 451–500 of 506 papers

TitleStatusHype
Rule Extraction in Unsupervised Anomaly Detection for Model Explainability: Application to OneClass SVMCode0
Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent EmbeddingsCode0
Deep Weakly-supervised Anomaly DetectionCode0
Rate-Distortion Optimization Guided Autoencoder for Isometric Embedding in Euclidean Latent Space—0
A Joint Model for IT Operation Series Prediction and Anomaly Detection—0
The Area of the Convex Hull of Sampled Curves: a Robust Functional Statistical Depth MeasureCode0
RATE-DISTORTION OPTIMIZATION GUIDED AUTOENCODER FOR GENERATIVE APPROACH—0
Deep End-to-end Unsupervised Anomaly Detection—0
Anomaly Detection with Inexact Labels—0
Detection of Backdoors in Trained Classifiers Without Access to the Training Set—0
Developing an Unsupervised Real-time Anomaly Detection Scheme for Time Series with Multi-seasonality—0
Learning Neural Representations for Network Anomaly DetectionCode0
Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural NetworkCode0
Batch Uniformization for Minimizing Maximum Anomaly Score of DNN-based Anomaly Detection in Sounds—0
Self-Attentive, Multi-Context One-Class Classification for Unsupervised Anomaly Detection on TextCode0
Using Self-Supervised Learning Can Improve Model Robustness and UncertaintyCode0
GAN-based Multiple Adjacent Brain MRI Slice Reconstruction for Unsupervised Alzheimer's Disease Diagnosis—0
UaiNets: From Unsupervised to Active Deep Anomaly Detection—0
GAN Augmented Text Anomaly Detection with Sequences of Deep Statistics—0
A Comparison Study of Credit Card Fraud Detection: Supervised versus Unsupervised—0
Supervised Anomaly Detection based on Deep Autoregressive Density Estimators—0
Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly DetectionCode0
Learning Representations from Healthcare Time Series Data for Unsupervised Anomaly Detection—0
Unsupervised Contextual Anomaly Detection using Joint Deep Variational Generative Models—0
Robust Subspace Recovery Layer for Unsupervised Anomaly DetectionCode0
adVAE: A self-adversarial variational autoencoder with Gaussian anomaly prior knowledge for anomaly detectionCode0
ELKI: A large open-source library for data analysis - ELKI Release 0.7.5 "Heidelberg"—0
f-AnoGAN: Fast Unsupervised Anomaly Detection with Generative Adversarial NetworksCode0
Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly DetectionCode0
DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time SeriesCode0
Unsupervised Anomaly Detection in Energy Time Series Data Using Variational Recurrent Autoencoders with Attention—0
Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection—0
AdaFlow: Domain-Adaptive Density Estimator with Application to Anomaly Detection and Unpaired Cross-Domain Translation—0
A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series DataCode0
Unsupervised Detection of Anomalous Sound based on Deep Learning and the Neyman-Pearson LemmaCode0
DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GAN—0
Band selection with Higher Order Multivariate Cumulants for small target detection in hyperspectral imagesCode0
Anomaly Detection Using GANs for Visual Inspection in Noisy Training Data—0
Deep Active Learning for Anomaly Detection—0
Sequence Aggregation Rules for Anomaly Detection in Computer Network Traffic—0
Incorporating Privileged Information to Unsupervised Anomaly Detection—0
Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier featuresCode0
Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR ImagesCode0
Clustering and Unsupervised Anomaly Detection with L2 Normalized Deep Auto-Encoder Representations—0
A Theoretical Investigation of Graph Degree as an Unsupervised Normality Measure—0
Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly DetectionCode0
Squeezed Convolutional Variational AutoEncoder for Unsupervised Anomaly Detection in Edge Device Industrial Internet of Things—0
Arrays of (locality-sensitive) Count Estimators (ACE): High-Speed Anomaly Detection via Cache Lookups—0
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker DiscoveryCode0
Latent Laplacian Maximum Entropy Discrimination for Detection of High-Utility Anomalies—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ACR-NTL (zero-shot, test anomaly ratio=1%)ROC-AUC FAR62.5—Unverified
2ACR-DSVDD (zero-shot, anomaly ratio=1%)ROC-AUC FAR62—Unverified
3ACR-NTL (zero-shot, test anomaly ratio=20%)ROC-AUC FAR62—Unverified
4ACR-DSVDD (zero-shot, anomaly ratio=20%)ROC-AUC FAR59.1—Unverified
5COPODROC-AUC FAR50.42—Unverified
6OC-SVMROC-AUC FAR49.57—Unverified
7SO-GAALROC-AUC FAR49.35—Unverified
8ECOD Li et al. (2022)ROC-AUC FAR49.19—Unverified
9LOFROC-AUC FAR34.96—Unverified
10deepSVDDROC-AUC FAR34.53—Unverified
#ModelMetricClaimedVerifiedStatus
1DFM (flow matching)F194.1—Unverified
2ContextFlow++ (Glow-based)F193.62—Unverified
3TranAdF189.15—Unverified
4MTAD-GATF188.8—Unverified
5CAE-MF188.27—Unverified
6OmniAnomalyF187.28—Unverified
7GlowF186.05—Unverified
8GDNF185.18—Unverified
9USADF181.86—Unverified
#ModelMetricClaimedVerifiedStatus
1SOMAUC65.43—Unverified
2Isolation ForestAUC59.42—Unverified
3Latent Outlier ExposureAUC58.59—Unverified
4NeuTraL-ADAUC57.03—Unverified
5RSRAEAUC55.38—Unverified
6SOM-DAGMMAUC53.82—Unverified
7Local Outlier FactorAUC52.86—Unverified
8One Class Support Vector MachinesAUC51.68—Unverified
9DAGMMAUC51.22—Unverified
#ModelMetricClaimedVerifiedStatus
1RSRAEAUC-ROC0.85—Unverified
2RSRAEAUC (outlier ratio = 0.5)0.83—Unverified
3RSRAEAUC-ROC0.75—Unverified
4RSRAEAUC-ROC0.69—Unverified
5RSRAEAUC-ROC0.69—Unverified
#ModelMetricClaimedVerifiedStatus
1Semi-orthogonalSegmentation AUROC98.1—Unverified
2WeakREST-UnSegmentation AP76.9—Unverified
3DSRSegmentation AP61.4—Unverified
#ModelMetricClaimedVerifiedStatus
1RSRAEAUC (outlier ratio = 0.5)0.83—Unverified
#ModelMetricClaimedVerifiedStatus
1MSFRDetection AUROC87.1—Unverified
#ModelMetricClaimedVerifiedStatus
1RSRAEAUC (outlier ratio = 0.5)0.77—Unverified
#ModelMetricClaimedVerifiedStatus
1DiffusionADDetection AUROC99.6—Unverified
#ModelMetricClaimedVerifiedStatus
1VRAE+SVMAUC0.98—Unverified
#ModelMetricClaimedVerifiedStatus
1Semi-orthogonalSegmentation AUROC96—Unverified
#ModelMetricClaimedVerifiedStatus
1LVADAUROC0.94—Unverified
#ModelMetricClaimedVerifiedStatus
1DyEdgeGATAUC0.8—Unverified
#ModelMetricClaimedVerifiedStatus
1RSRAEAUC (outlier ratio = 0.5)0.85—Unverified
#ModelMetricClaimedVerifiedStatus
1TranADPrecision92.62—Unverified
#ModelMetricClaimedVerifiedStatus
1LVADAUC-ROC1—Unverified
#ModelMetricClaimedVerifiedStatus
1DyEdgeGATAUC0.83—Unverified
#ModelMetricClaimedVerifiedStatus
1P-CAE W-MSE (Tilted View)AUROC78.1—Unverified