SOTAVerified

Anomaly Detection

Anomaly Detection is a binary classification identifying unusual or unexpected patterns in a dataset, which deviate significantly from the majority of the data. The goal of anomaly detection is to identify such anomalies, which could represent errors, fraud, or other types of unusual events, and flag them for further investigation.

[Image source]: GAN-based Anomaly Detection in Imbalance Problems

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1876–1900 of 4856 papers

TitleStatusHype
M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoising—0
Enhancing Fairness in Unsupervised Graph Anomaly Detection through DisentanglementCode0
An Origami-Inspired Endoscopic Capsule with Tactile Perception for Early Tissue Anomaly Detection—0
Anomaly Anything: Promptable Unseen Visual Anomaly Generation—0
SAM-LAD: Segment Anything Model Meets Zero-Shot Logic Anomaly Detection—0
ContextFlow++: Generalist-Specialist Flow-based Generative Models with Mixed-Variable Context EncodingCode0
GLADformer: A Mixed Perspective for Graph-level Anomaly Detection—0
Stochastic Earned Value Analysis using Monte Carlo Simulation and Statistical Learning Techniques—0
Anomaly Detection in Dynamic Graphs: A Comprehensive Survey—0
Joint Selective State Space Model and Detrending for Robust Time Series Anomaly DetectionCode0
Performance Examination of Symbolic Aggregate Approximation in IoT Applications—0
From Zero to Hero: Cold-Start Anomaly DetectionCode0
Anomaly Detection by Context Contrasting—0
Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled DataCode0
A Mallows-like Criterion for Anomaly Detection with Random Forest Implementation—0
Video Anomaly Detection in 10 Years: A Survey and Outlook—0
Anomaly detection for the identification of volcanic unrest in satellite imagery—0
When and How Does In-Distribution Label Help Out-of-Distribution Detection?Code0
Anomaly Detection Using Normalizing Flow-Based Density Estimation and Synthetic Defect ClassificationCode0
SmoothGNN: Smoothing-aware GNN for Unsupervised Node Anomaly Detection—0
A Study on Unsupervised Anomaly Detection and Defect Localization using Generative Model in Ultrasonic Non-Destructive Testing—0
KiNETGAN: Enabling Distributed Network Intrusion Detection through Knowledge-Infused Synthetic Data Generation—0
Acquiring Better Load Estimates by Combining Anomaly and Change Point Detection in Power Grid Time-series MeasurementsCode0
Secure Hierarchical Federated Learning in Vehicular Networks Using Dynamic Client Selection and Anomaly Detection—0
Qsco: A Quantum Scoring Module for Open-set Supervised Anomaly Detection—0
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All datasetsMVTec ADVisAMVTec LOCO ADOne-class CIFAR-10CUHK AvenueShanghaiTechFishyscapes L&FUCR Anomaly ArchiveMPDDBTADOne-class CIFAR-100UBnormal

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016—Unverified
2CPR-fast(TensorRT)FPS362—Unverified
3CPR(TensorRT)FPS130—Unverified
4GLASSDetection AUROC99.9—Unverified
5UniNetDetection AUROC99.9—Unverified
6HETMMDetection AUROC99.8—Unverified
7INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9DDADDetection AUROC99.8—Unverified
10PBASDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9—Unverified
5DDADDetection AUROC98.9—Unverified
6Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
7DiffusionADDetection AUROC98.8—Unverified
8GLASSDetection AUROC98.8—Unverified
9TransFusionDetection AUROC98.7—Unverified
10HETMMDetection AUROC98.1—Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3—Unverified
2PSADAvg. Detection AUROC94.9—Unverified