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 401–450 of 506 papers

TitleStatusHype
DRAM Failure Prediction in AIOps: Empirical Evaluation, Challenges and Opportunities—0
Image Synthesis as a Pretext for Unsupervised Histopathological Diagnosis—0
ODDObjects: A Framework for Multiclass Unsupervised Anomaly Detection on Masked ObjectsCode0
Unsupervised anomaly detection for a Smart Autonomous Robotic Assistant Surgeon (SARAS)using a deep residual autoencoder—0
Autoencoders for unsupervised anomaly detection in high energy physics—0
Concentration bounds for the empirical angular measure with statistical learning applicationsCode0
Detecting Anomalies Through Contrast in Heterogeneous Data—0
3D Reasoning for Unsupervised Anomaly Detection in Pediatric WbMRI—0
Anomaly Detection using Principles of Human PerceptionCode0
Unsupervised anomaly detection in digital pathology using GANs—0
Flow-based Self-supervised Density Estimation for Anomalous Sound Detection—0
A new interpretable unsupervised anomaly detection method based on residual explanation—0
Bump Hunting in Latent SpaceCode0
Unsupervised Brain Anomaly Detection and Segmentation with Transformers—0
Leveraging 3D Information in Unsupervised Brain MRI Segmentation—0
Double-Adversarial Activation Anomaly Detection: Adversarial Autoencoders are Anomaly GeneratorsCode0
Deep unsupervised anomaly detection—0
Iterative Image Inpainting with Structural Similarity Mask for Anomaly Detection—0
Learning Deep Latent Variable Models via Amortized Langevin Dynamics—0
Understanding Bias in Anomaly Detection: A Semi-Supervised View with PAC GuaranteesCode0
GenAD: General Representations of Multivariate Time Series for Anomaly Detection—0
A General Framework for Unsupervised Anomaly Detection—0
Unsupervised Anomaly Detectors to Detect Intrusions in the Current Threat Landscape—0
Anomaly detection through latent space restoration using vector-quantized variational autoencoders—0
Unsupervised Anomaly Detection From Semantic Similarity Scores—0
Image-based Plant Disease Diagnosis with Unsupervised Anomaly Detection Based on Reconstructability of Colors—0
Dependency-based Anomaly Detection: a General Framework and Comprehensive Evaluation—0
UAV-AdNet: Unsupervised Anomaly Detection using Deep Neural Networks for Aerial Surveillance—0
Unsupervised Anomaly Detection in Parole Hearings using Language Models—0
Graph Regularized Autoencoder and its Application in Unsupervised Anomaly Detection—0
Interpretable Machine Learning Models for Predicting and Explaining Vehicle Fuel Consumption Anomalies—0
Dimensionality Reduction and Anomaly Detection for CPPS Data using Autoencoder—0
Anomaly detection with superexperts under delayed feedbackCode0
Deep Autoencoding GMM-based Unsupervised Anomaly Detection in Acoustic Signals and its Hyper-parameter Optimization—0
Unsupervised Anomaly Detection on Temporal Multiway Data—0
Label-Free Segmentation of COVID-19 Lesions in Lung CT—0
Anomaly Detection by Recombining Gated Unsupervised ExpertsCode0
MADGAN: unsupervised Medical Anomaly Detection GAN using multiple adjacent brain MRI slice reconstruction—0
Unsupervised anomaly detection for discrete sequence healthcare data—0
P-KDGAN: Progressive Knowledge Distillation with GANs for One-class Novelty Detection—0
ID-Conditioned Auto-Encoder for Unsupervised Anomaly Detection—0
Categorical anomaly detection in heterogeneous data using minimum description length clustering—0
Spoof Face Detection Via Semi-Supervised Adversarial Training—0
Temporal signals to images: Monitoring the condition of industrial assets with deep learning image processing algorithms—0
Anomaly Detection with Density EstimationCode0
Data Augmentation by AutoEncoders for Unsupervised Anomaly Detection—0
Unsupervised Anomaly Detection in Stream Data with Online Evolving Spiking Neural Networks—0
Deep Autoencoders with Value-at-Risk Thresholding for Unsupervised Anomaly Detection—0
Transfer Learning from an Auxiliary Discriminative Task for Unsupervised Anomaly Detection—0
Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative NetworkCode0
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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