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 151–200 of 506 papers

TitleStatusHype
Unsupervised anomaly detection on cybersecurity data streams: a case with BETH dataset—0
When Unsupervised Domain Adaptation meets One-class Anomaly Detection: Addressing the Two-fold Unsupervised Curse by Leveraging Anomaly Scarcity—0
A Radon-Nikodým Perspective on Anomaly Detection: Theory and Implications—0
An Expert Ensemble for Detecting Anomalous Scenes, Interactions, and Behaviors in Autonomous Driving—0
A Synergy Scoring Filter for Unsupervised Anomaly Detection with Noisy Data—0
Statistically Significant kNNAD by Selective Inference—0
Unsupervised Anomaly Detection through Mass Repulsing Optimal TransportCode0
Unsupervised Anomaly Detection on Implicit Shape representations for Sarcopenia Detection—0
Position: Untrained Machine Learning for Anomaly Detection—0
Finding Pegasus: Enhancing Unsupervised Anomaly Detection in High-Dimensional Data using a Manifold-Based Approach—0
Calibrated Unsupervised Anomaly Detection in Multivariate Time-series using Reinforcement Learning—0
Anomaly Detection via Autoencoder Composite Features and NCE—0
GDformer: Going Beyond Subsequence Isolation for Multivariate Time Series Anomaly DetectionCode0
Detecting Anomalies Using Rotated Isolation Forest—0
Towards Automated Self-Supervised Learning for Truly Unsupervised Graph Anomaly DetectionCode0
Score Combining for Contrastive OOD Detection—0
InDeed: Interpretable image deep decomposition with guaranteed generalizability—0
A Unified Latent Schrodinger Bridge Diffusion Model for Unsupervised Anomaly Detection and Localization—0
DFM: Differentiable Feature Matching for Anomaly Detection—0
An Unsupervised Anomaly Detection in Electricity Consumption Using Reinforcement Learning and Time Series Forest Based Framework—0
Unsupervised Anomaly Detection for Tabular Data Using Noise Evaluation—0
F-RBA: A Federated Learning-based Framework for Risk-based Authentication—0
Self-Supervised Masked Mesh Learning for Unsupervised Anomaly Detection on 3D Cortical Surfaces—0
SCADE: Scalable Framework for Anomaly Detection in High-Performance System—0
State Frequency Estimation for Anomaly Detection—0
Unsupervised Learning Approach to Anomaly Detection in Gravitational Wave Data—0
A Machine Learning-based Anomaly Detection Framework in Life Insurance Contracts—0
PATH: A Discrete-sequence Dataset for Evaluating Online Unsupervised Anomaly Detection Approaches for Multivariate Time SeriesCode0
Steam Turbine Anomaly Detection: An Unsupervised Learning Approach Using Enhanced Long Short-Term Memory Variational Autoencoder—0
Deep Autoencoders for Unsupervised Anomaly Detection in Wildfire Prediction—0
Synomaly Noise and Multi-Stage Diffusion: A Novel Approach for Unsupervised Anomaly Detection in Ultrasound ImagingCode0
Adaptive NAD: Online and Self-adaptive Unsupervised Network Anomaly DetectorCode0
DFM: Interpolant-free Dual Flow Matching—0
Selective Test-Time Adaptation for Unsupervised Anomaly Detection using Neural Implicit RepresentationsCode0
HyperBrain: Anomaly Detection for Temporal Hypergraph Brain NetworksCode0
Vision-Language Models Assisted Unsupervised Video Anomaly Detection—0
Enhancing Anomaly Detection via Generating Diversified and Hard-to-distinguish Synthetic Anomalies—0
Optimal Classification-based Anomaly Detection with Neural Networks: Theory and PracticeCode0
Context Enhancement with Reconstruction as Sequence for Unified Unsupervised Anomaly DetectionCode0
Texture-AD: An Anomaly Detection Dataset and Benchmark for Real Algorithm Development—0
Memoryless Multimodal Anomaly Detection via Student-Teacher Network and Signed Distance Learning—0
Adapted-MoE: Mixture of Experts with Test-Time Adaption for Anomaly Detection—0
Unsupervised Anomaly Detection and Localization with Generative Adversarial Networks—0
Attention-Guided Perturbation for Unsupervised Image Anomaly Detection—0
Impact of Inaccurate Contamination Ratio on Robust Unsupervised Anomaly Detection—0
Hybrid Efficient Unsupervised Anomaly Detection for Early Pandemic Case Identification—0
FedAD-Bench: A Unified Benchmark for Federated Unsupervised Anomaly Detection in Tabular Data—0
Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detection—0
Online Model-based Anomaly Detection in Multivariate Time Series: Taxonomy, Survey, Research Challenges and Future Directions—0
Diffusion Models for Unsupervised Anomaly Detection in Fetal Brain Ultrasound—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