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 251–300 of 506 papers

TitleStatusHype
Categorical anomaly detection in heterogeneous data using minimum description length clustering—0
Challenges for Unsupervised Anomaly Detection in Particle Physics—0
CL-Flow:Strengthening the Normalizing Flows by Contrastive Learning for Better Anomaly Detection—0
Clustering and Unsupervised Anomaly Detection with L2 Normalized Deep Auto-Encoder Representations—0
Coincident Learning for Unsupervised Anomaly Detection—0
Constructing a meta-learner for unsupervised anomaly detection—0
Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection—0
CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localization—0
Cross Attention Transformers for Multi-modal Unsupervised Whole-Body PET Anomaly Detection—0
Data augmentation and pre-trained networks for extremely low data regimes unsupervised visual inspection—0
Data Augmentation by AutoEncoders for Unsupervised Anomaly Detection—0
Deep Anomaly Detection in Text—0
Deep Anomaly Detection on Tennessee Eastman Process Data—0
Deep Autoencoders for Unsupervised Anomaly Detection in Wildfire Prediction—0
Deep Autoencoders with Value-at-Risk Thresholding for Unsupervised Anomaly Detection—0
Deep Autoencoding GMM-based Unsupervised Anomaly Detection in Acoustic Signals and its Hyper-parameter Optimization—0
Deep End-to-end Unsupervised Anomaly Detection—0
Deep Federated Anomaly Detection for Multivariate Time Series Data—0
Deep Learning-Based Anomaly Detection in Synthetic Aperture Radar Imaging—0
Deep learning for structural health monitoring: An application to heritage structures—0
Deep Random Projection Outlyingness for Unsupervised Anomaly Detection—0
Deep Semi-Supervised Anomaly Detection for Finding Fraud in the Futures Market—0
Deep unsupervised anomaly detection—0
Dependency-based Anomaly Detection: a General Framework and Comprehensive Evaluation—0
DETECTA 2.0: Research into non-intrusive methodologies supported by Industry 4.0 enabling technologies for predictive and cyber-secure maintenance in SMEs—0
Detecting Anomalies Through Contrast in Heterogeneous Data—0
Detecting Anomalies Using Rotated Isolation Forest—0
Detecting Relative Anomaly—0
DFM: Differentiable Feature Matching for Anomaly Detection—0
DFM: Interpolant-free Dual Flow Matching—0
Diffusion Models for Unsupervised Anomaly Detection in Fetal Brain Ultrasound—0
Dimensionality Reduction and Anomaly Detection for CPPS Data using Autoencoder—0
Diminishing Empirical Risk Minimization for Unsupervised Anomaly Detection—0
Disentangling Physical Parameters for Anomalous Sound Detection Under Domain Shifts—0
Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detection—0
Do Deep Neural Networks Contribute to Multivariate Time Series Anomaly Detection?—0
DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GAN—0
DRAM Failure Prediction in AIOps: Empirical Evaluation, Challenges and Opportunities—0
Dual-Branch Reconstruction Network for Industrial Anomaly Detection with RGB-D Data—0
Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detection—0
Dual-stream spatiotemporal networks with feature sharing for monitoring animals in the home cage—0
Dual-Student Knowledge Distillation Networks for Unsupervised Anomaly Detection—0
Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformers—0
ELKI: A large open-source library for data analysis - ELKI Release 0.7.5 "Heidelberg"—0
Enhancing Anomaly Detection via Generating Diversified and Hard-to-distinguish Synthetic Anomalies—0
Env-Aware Anomaly Detection: Ignore Style Changes, Stay True to Content!—0
Evaluation of Color Anomaly Detection in Multispectral Images For Synthetic Aperture Sensing—0
Excision And Recovery: Visual Defect Obfuscation Based Self-Supervised Anomaly Detection Strategy—0
Explainable Unsupervised Anomaly Detection with Random Forest—0
Fairness-aware Anomaly Detection via Fair Projection—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