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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 401–450 of 506 papers

TitleStatusHype
Unsupervised Recycled FPGA Detection Using Symmetry Analysis—0
Unsupervised Surface Anomaly Detection with Diffusion Probabilistic Model—0
uTRAND: Unsupervised Anomaly Detection in Traffic Trajectories—0
View-Invariant Pixelwise Anomaly Detection in Multi-object Scenes with Adaptive View Synthesis—0
What makes a good data augmentation for few-shot unsupervised image anomaly detection?—0
When Unsupervised Domain Adaptation meets One-class Anomaly Detection: Addressing the Two-fold Unsupervised Curse by Leveraging Anomaly Scarcity—0
Ymir: A Supervised Ensemble Framework for Multivariate Time Series Anomaly Detection—0
Contaminated Multivariate Time-Series Anomaly Detection with Spatio-Temporal Graph Conditional Diffusion Models—0
Non-contact Sensing for Anomaly Detection in Wind Turbine Blades: A focus-SVDD with Complex-Valued Auto-Encoder Approach—0
No Shifted Augmentations (NSA): compact distributions for robust self-supervised Anomaly Detection—0
Language Models Meet Anomaly Detection for Better Interpretability and GeneralizabilityCode0
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker DiscoveryCode0
MiniMaxAD: A Lightweight Autoencoder for Feature-Rich Anomaly DetectionCode0
Neural Network Training Strategy to Enhance Anomaly Detection Performance: A Perspective on Reconstruction Loss AmplificationCode0
The Analysis of Online Event Streams: Predicting the Next Activity for Anomaly DetectionCode0
Normality-Calibrated Autoencoder for Unsupervised Anomaly Detection on Data ContaminationCode0
The Area of the Convex Hull of Sampled Curves: a Robust Functional Statistical Depth MeasureCode0
Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative NetworkCode0
Adaptive NAD: Online and Self-adaptive Unsupervised Network Anomaly DetectorCode0
ODDObjects: A Framework for Multiclass Unsupervised Anomaly Detection on Masked ObjectsCode0
Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly DetectionCode0
M^2AD: Multi-Sensor Multi-System Anomaly Detection through Global Scoring and Calibrated ThresholdingCode0
DSV: An Alignment Validation Loss for Self-supervised Outlier Model SelectionCode0
Double-Adversarial Activation Anomaly Detection: Adversarial Autoencoders are Anomaly GeneratorsCode0
Optimal Classification-based Anomaly Detection with Neural Networks: Theory and PracticeCode0
Time series anomaly detection with reconstruction-based state-space modelsCode0
Leveraging the Mahalanobis Distance to enhance Unsupervised Brain MRI Anomaly DetectionCode0
Less-supervised learning with knowledge distillation for sperm morphology analysisCode0
Unsupervised Anomaly Detection in Medical Images with a Memory-augmented Multi-level Cross-attentional Masked AutoencoderCode0
TiWS-iForest: Isolation Forest in Weakly Supervised and Tiny ML scenariosCode0
Attend, Distill, Detect: Attention-aware Entropy Distillation for Anomaly DetectionCode0
DISYRE: Diffusion-Inspired SYnthetic REstoration for Unsupervised Anomaly DetectionCode0
Learning Neural Representations for Network Anomaly DetectionCode0
Position Regression for Unsupervised Anomaly DetectionCode0
Towards Automated Self-Supervised Learning for Truly Unsupervised Graph Anomaly DetectionCode0
Towards frugal unsupervised detection of subtle abnormalities in medical imagingCode0
Towards Phytoplankton Parasite Detection Using AutoencodersCode0
Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time seriesCode0
Image-Pointcloud Fusion based Anomaly Detection using PD-REAL DatasetCode0
Pyramid-based Mamba Multi-class Unsupervised Anomaly DetectionCode0
Quorum: Zero-Training Unsupervised Anomaly Detection using Quantum AutoencodersCode0
Using Self-Supervised Learning Can Improve Model Robustness and UncertaintyCode0
Distillation-based fabric anomaly detectionCode0
Diffusion Models with Ensembled Structure-Based Anomaly Scoring for Unsupervised Anomaly DetectionCode0
HyperBrain: Anomaly Detection for Temporal Hypergraph Brain NetworksCode0
Anomaly detection with superexperts under delayed feedbackCode0
Deep Weakly-supervised Anomaly DetectionCode0
HSS-IAD: A Heterogeneous Same-Sort Industrial Anomaly Detection DatasetCode0
How to Evaluate the Quality of Unsupervised Anomaly Detection Algorithms?Code0
Quadratic Neuron-empowered Heterogeneous Autoencoder for Unsupervised Anomaly DetectionCode0
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All datasetsAnoShiftSMAPVehicle ClaimsFashion-MNISTKolektorSDD220NEWSAeBAD-SCaltech-101DAGM2007ECG5000KolektorSDDMNIST

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