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 351–400 of 506 papers

TitleStatusHype
Anomaly Detection: How to Artificially Increase your F1-Score with a Biased Evaluation ProtocolCode0
TS2Vec: Towards Universal Representation of Time SeriesCode1
Anomaly Detection and Automated Labeling for Voter Registration File Changes—0
Towards Total Recall in Industrial Anomaly DetectionCode2
Graph Neural Network-Based Anomaly Detection in Multivariate Time SeriesCode1
Unsupervised Anomaly Detection Ensembles using Item Response TheoryCode0
Self-supervise, Refine, Repeat: Improving Unsupervised Anomaly Detection—0
From Unsupervised to Semi-supervised Anomaly Detection Methods for HRRP Targets—0
Spot the Difference: Detection of Topological Changes via Geometric AlignmentCode0
Implicit field learning for unsupervised anomaly detection in medical imagesCode1
Deep Random Projection Outlyingness for Unsupervised Anomaly Detection—0
MemStream: Memory-Based Streaming Anomaly DetectionCode1
Self-supervised Lesion Change Detection and Localisation in Longitudinal Multiple Sclerosis Brain Imaging—0
Data augmentation and pre-trained networks for extremely low data regimes unsupervised visual inspection—0
Semi-orthogonal Embedding for Efficient Unsupervised Anomaly SegmentationCode1
Anomaly Detection in Predictive Maintenance: A New Evaluation Framework for Temporal Unsupervised Anomaly Detection Algorithms—0
Conformal Anomaly Detection on Spatio-Temporal Observations with Missing DataCode1
Unsupervised Anomaly Detection in MR Images using Multi-Contrast Information—0
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
CutPaste: Self-Supervised Learning for Anomaly Detection and LocalizationCode1
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
Flow-based Self-supervised Density Estimation for Anomalous Sound Detection—0
Unsupervised anomaly detection in digital pathology using GANs—0
A new interpretable unsupervised anomaly detection method based on residual explanation—0
Bump Hunting in Latent SpaceCode0
Student-Teacher Feature Pyramid Matching for Anomaly DetectionCode1
Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical ImagesCode1
Unsupervised Brain Anomaly Detection and Segmentation with Transformers—0
Clustered Hierarchical Anomaly and Outlier Detection AlgorithmsCode1
[Re] Learning Memory Guided Normality for Anomaly DetectionCode1
Leveraging 3D Information in Unsupervised Brain MRI Segmentation—0
Deep One-Class Classification via Interpolated Gaussian DescriptorCode1
Double-Adversarial Activation Anomaly Detection: Adversarial Autoencoders are Anomaly GeneratorsCode0
Deep unsupervised anomaly detection—0
GenAD: General Representations of Multivariate Time Series for 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
Unsupervised Anomaly Detection by Robust Collaborative AutoencodersCode1
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
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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