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 451–500 of 506 papers

TitleStatusHype
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
Detecting Relative Anomaly—0
Unsupervised Abnormal Traffic Detection through Topological Flow Analysis—0
A general-purpose method for applying Explainable AI for Anomaly Detection—0
Detecting Anomalies Using Rotated Isolation Forest—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
Deep Active Learning for Anomaly Detection—0
A General Framework for Unsupervised Anomaly Detection—0
Env-Aware Anomaly Detection: Ignore Style Changes, Stay True to Content!—0
Detecting Anomalies Through Contrast in Heterogeneous Data—0
Unsupervised Anomaly Detection and Localization with Generative Adversarial Networks—0
Evaluation of Color Anomaly Detection in Multispectral Images For Synthetic Aperture Sensing—0
Adversarial Denoising Diffusion Model for Unsupervised Anomaly Detection—0
Excision And Recovery: Visual Defect Obfuscation Based Self-Supervised Anomaly Detection Strategy—0
Ymir: A Supervised Ensemble Framework for Multivariate Time Series Anomaly Detection—0
Explainable Unsupervised Anomaly Detection with Random Forest—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
Exploring Dual Model Knowledge Distillation for Anomaly Detection—0
Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detection—0
F2PAD: A General Optimization Framework for Feature-Level to Pixel-Level Anomaly Detection—0
Dependency-based Anomaly Detection: a General Framework and Comprehensive Evaluation—0
Unsupervised anomaly detection for a Smart Autonomous Robotic Assistant Surgeon (SARAS)using a deep residual autoencoder—0
AD-MERCS: Modeling Normality and Abnormality in Unsupervised Anomaly Detection—0
Deep unsupervised anomaly detection—0
Deep Semi-Supervised Anomaly Detection for Finding Fraud in the Futures Market—0
Unsupervised anomaly detection for discrete sequence healthcare data—0
FedAD-Bench: A Unified Benchmark for Federated Unsupervised Anomaly Detection in Tabular Data—0
Deep Random Projection Outlyingness for Unsupervised Anomaly Detection—0
Finding Pegasus: Enhancing Unsupervised Anomaly Detection in High-Dimensional Data using a Manifold-Based Approach—0
Deep learning for structural health monitoring: An application to heritage structures—0
Flow-based Self-supervised Density Estimation for Anomalous Sound Detection—0
Focus Your Distribution: Coarse-to-Fine Non-Contrastive Learning for Anomaly Detection and Localization—0
Forecast-based Multi-aspect Framework for Multivariate Time-series Anomaly Detection—0
Forensic Data Analytics for Anomaly Detection in Evolving Networks—0
F-RBA: A Federated Learning-based Framework for Risk-based Authentication—0
Unsupervised Anomaly Detection for Tabular Data Using Noise Evaluation—0
From Unsupervised to Semi-supervised Anomaly Detection Methods for HRRP Targets—0
Deep Learning-Based Anomaly Detection in Synthetic Aperture Radar Imaging—0
Deep Federated Anomaly Detection for Multivariate Time Series Data—0
Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detection—0
GADY: Unsupervised Anomaly Detection on Dynamic Graphs—0
GAN Augmented Text Anomaly Detection with Sequences of Deep Statistics—0
GAN-based Multiple Adjacent Brain MRI Slice Reconstruction for Unsupervised Alzheimer's Disease Diagnosis—0
GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models—0
G-CMP: Graph-enhanced Contextual Matrix Profile for unsupervised anomaly detection in sensor-based remote health monitoring—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