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 1–50 of 506 papers

TitleStatusHype
Conditional diffusion models for guided anomaly detection in brain images using fluid-driven anomaly randomization—0
RoBiS: Robust Binary Segmentation for High-Resolution Industrial ImagesCode1
Learning Normal Patterns in Musical Loops—0
Unsupervised anomaly detection in MeV ultrafast electron diffraction—0
Fairness-aware Anomaly Detection via Fair Projection—0
ADALog: Adaptive Unsupervised Anomaly detection in Logs with Self-attention Masked Language Model—0
GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models—0
CostFilter-AD: Enhancing Anomaly Detection through Matching Cost FilteringCode2
Statistical Inference for Clustering-based Anomaly Detection—0
Explainable Unsupervised Anomaly Detection with Random Forest—0
Memory-Augmented Dual-Decoder Networks for Multi-Class Unsupervised Anomaly Detection—0
M^2AD: Multi-Sensor Multi-System Anomaly Detection through Global Scoring and Calibrated ThresholdingCode0
HSS-IAD: A Heterogeneous Same-Sort Industrial Anomaly Detection DatasetCode0
Sliced-Wasserstein Distance-based Data Selection—0
Quorum: Zero-Training Unsupervised Anomaly Detection using Quantum AutoencodersCode0
ACMamba: Fast Unsupervised Anomaly Detection via An Asymmetrical Consensus State Space Model—0
Strengthening Anomaly Awareness—0
Pyramid-based Mamba Multi-class Unsupervised Anomaly DetectionCode0
TailedCore: Few-Shot Sampling for Unsupervised Long-Tail Noisy Anomaly DetectionCode1
Unsupervised Anomaly Detection in Multivariate Time Series across Heterogeneous DomainsCode0
Omni-AD: Learning to Reconstruct Global and Local Features for Multi-class Anomaly DetectionCode1
The MVTec AD 2 Dataset: Advanced Scenarios for Unsupervised Anomaly DetectionCode1
Post-Hoc Calibrated Anomaly Detection—0
Correcting Deviations from Normality: A Reformulated Diffusion Model for Multi-Class Unsupervised Anomaly DetectionCode2
Scale-Aware Contrastive Reverse Distillation for Unsupervised Medical Anomaly DetectionCode0
U2AD: Uncertainty-based Unsupervised Anomaly Detection Framework for Detecting T2 Hyperintensity in MRI Spinal CordCode0
ISP-AD: A Large-Scale Real-World Dataset for Advancing Industrial Anomaly Detection with Synthetic and Real Defects—0
Unsupervised anomaly detection on cybersecurity data streams: a case with BETH dataset—0
When Unsupervised Domain Adaptation meets One-class Anomaly Detection: Addressing the Two-fold Unsupervised Curse by Leveraging Anomaly Scarcity—0
A Radon-Nikodým Perspective on Anomaly Detection: Theory and Implications—0
MAD-AD: Masked Diffusion for Unsupervised Brain Anomaly DetectionCode1
An Expert Ensemble for Detecting Anomalous Scenes, Interactions, and Behaviors in Autonomous Driving—0
A Synergy Scoring Filter for Unsupervised Anomaly Detection with Noisy Data—0
Unsupervised Anomaly Detection through Mass Repulsing Optimal TransportCode0
Statistically Significant kNNAD by Selective Inference—0
Unsupervised Anomaly Detection on Implicit Shape representations for Sarcopenia Detection—0
3CAD: A Large-Scale Real-World 3C Product Dataset for Unsupervised AnomalyCode2
Position: Untrained Machine Learning for Anomaly Detection—0
Finding Pegasus: Enhancing Unsupervised Anomaly Detection in High-Dimensional Data using a Manifold-Based Approach—0
Calibrated Unsupervised Anomaly Detection in Multivariate Time-series using Reinforcement Learning—0
Anomaly Detection via Autoencoder Composite Features and NCE—0
GDformer: Going Beyond Subsequence Isolation for Multivariate Time Series Anomaly DetectionCode0
Detecting Anomalies Using Rotated Isolation Forest—0
Towards Automated Self-Supervised Learning for Truly Unsupervised Graph Anomaly DetectionCode0
Score Combining for Contrastive OOD Detection—0
Towards Accurate Unified Anomaly SegmentationCode1
InDeed: Interpretable image deep decomposition with guaranteed generalizability—0
DFM: Differentiable Feature Matching for Anomaly Detection—0
A Unified Latent Schrodinger Bridge Diffusion Model for Unsupervised Anomaly Detection and Localization—0
An Unsupervised Anomaly Detection in Electricity Consumption Using Reinforcement Learning and Time Series Forest Based Framework—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