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 351–375 of 506 papers

TitleStatusHype
An optimization method for out-of-distribution anomaly detection models—0
Removing Anomalies as Noises for Industrial Defect Localization—0
Resilient VAE: Unsupervised Anomaly Detection at the SLAC Linac Coherent Light Source—0
Detection of Backdoors in Trained Classifiers Without Access to the Training Set—0
Revisiting randomized choices in isolation forests—0
Anomaly Detection with Inexact Labels—0
A Comparison Study of Credit Card Fraud Detection: Supervised versus Unsupervised—0
Unsupervised Anomaly Detection via Masked Diffusion Posterior Sampling—0
Unsupervised Anomaly Detection via Nonlinear Manifold Learning—0
RUAD: unsupervised anomaly detection in HPC systems—0
Conditional diffusion models for guided anomaly detection in brain images using fluid-driven anomaly randomization—0
SCADE: Scalable Framework for Anomaly Detection in High-Performance System—0
Unsupervised Anomaly Detection with Local-Sensitive VQVAE and Global-Sensitive Transformers—0
Unsupervised Anomaly Detectors to Detect Intrusions in the Current Threat Landscape—0
Score Combining for Contrastive OOD Detection—0
Scrutinizing Shipment Records To Thwart Illegal Timber Trade—0
A Comparative Study on Unsupervised Anomaly Detection for Time Series: Experiments and Analysis—0
Unsupervised Brain Anomaly Detection and Segmentation with Transformers—0
Unsupervised Contextual Anomaly Detection using Joint Deep Variational Generative Models—0
Self-Supervised Guided Segmentation Framework for Unsupervised Anomaly Detection—0
ACMamba: Fast Unsupervised Anomaly Detection via An Asymmetrical Consensus State Space Model—0
Self-supervised Lesion Change Detection and Localisation in Longitudinal Multiple Sclerosis Brain Imaging—0
Self-Supervised Masked Mesh Learning for Unsupervised Anomaly Detection on 3D Cortical Surfaces—0
Self-Supervised Masking for Unsupervised Anomaly Detection and Localization—0
Self-Supervision for Tackling Unsupervised Anomaly Detection: Pitfalls and Opportunities—0
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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