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 301–350 of 506 papers

TitleStatusHype
The Analysis of Online Event Streams: Predicting the Next Activity for Anomaly DetectionCode0
Unsupervised Anomaly Detection from Time-of-Flight Depth Images—0
Omni-frequency Channel-selection Representations for Unsupervised Anomaly DetectionCode1
Bayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detection—0
Beyond Dents and Scratches: Logical Constraints in Unsupervised Anomaly Detection and Localization—0
Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time SeriesCode3
Anomalib: A Deep Learning Library for Anomaly Detection—0
Hybridization of Capsule and LSTM Networks for unsupervised anomaly detection on multivariate data—0
Identifying Backdoor Attacks in Federated Learning via Anomaly Detection—0
Studying the Robustness of Anti-adversarial Federated Learning Models Detecting Cyberattacks in IoT Spectrum Sensors—0
StRegA: Unsupervised Anomaly Detection in Brain MRIs using a Compact Context-encoding Variational AutoencoderCode1
Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with Multi-Task Brain Age Prediction—0
Anomaly Detection via Reverse Distillation from One-Class EmbeddingCode2
Little Help Makes a Big Difference: Leveraging Active Learning to Improve Unsupervised Time Series Anomaly Detection—0
An Attention-based ConvLSTM Autoencoder with Dynamic Thresholding for Unsupervised Anomaly Detection in Multivariate Time Series—0
TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series DataCode2
Forecast-based Multi-aspect Framework for Multivariate Time-series Anomaly Detection—0
Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detection—0
Continual Learning for Unsupervised Anomaly Detection in Continuous Auditing of Financial Accounting DataCode1
The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and LocalizationCode1
Ymir: A Supervised Ensemble Framework for Multivariate Time Series Anomaly Detection—0
TiWS-iForest: Isolation Forest in Weakly Supervised and Tiny ML scenariosCode0
A Taxonomy of Anomalies in Log Data—0
SQUID: Deep Feature In-Painting for Unsupervised Anomaly DetectionCode1
SLA^2P: Self-supervised Anomaly Detection with Adversarial PerturbationCode1
FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing FlowsCode1
Disentangling Physical Parameters for Anomalous Sound Detection Under Domain Shifts—0
Markus Thill Temporal convolutional autoencoder for unsupervised anomaly detection in time series—0
Normality-Calibrated Autoencoder for Unsupervised Anomaly Detection on Data ContaminationCode0
Revisiting randomized choices in isolation forests—0
Patch vs. Global Image-Based Unsupervised Anomaly Detection in MR Brain Scans of Early Parkinsonian Patients—0
Memory-augmented Adversarial Autoencoders for Multivariate Time-series Anomaly Detection with Deep Reconstruction and Prediction—0
Challenges for Unsupervised Anomaly Detection in Particle Physics—0
Focus Your Distribution: Coarse-to-Fine Non-Contrastive Learning for Anomaly Detection and Localization—0
Fully Convolutional Cross-Scale-Flows for Image-based Defect DetectionCode1
No Shifted Augmentations (NSA): strong baselines for self-supervised Anomaly Detection—0
A2Log: Attentive Augmented Log Anomaly Detection—0
3-Dimensional Deep Learning with Spatial Erasing for Unsupervised Anomaly Segmentation in Brain MRI—0
Enhancing Unsupervised Anomaly Detection with Score-Guided NetworkCode0
Self-supervised Pseudo Multi-class Pre-training for Unsupervised Anomaly Detection and Segmentation in Medical ImagesCode1
DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectionCode1
Unsupervised Detection of Lung Nodules in Chest Radiography Using Generative Adversarial Networks—0
Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detection—0
Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals—0
CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing FlowsCode1
Tail of Distribution GAN (TailGAN): Generative-Adversarial-Network-Based Boundary Formation—0
Multi-Perspective Content Delivery Networks Security Framework Using Optimized Unsupervised Anomaly Detection—0
Boundary of Distribution Support Generator (BDSG): Sample Generation on the Boundary—0
Anomaly Detection via Self-organizing MapCode0
Canonical Polyadic Decomposition and Deep Learning for Machine Fault Detection—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