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

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Showing 276–300 of 4856 papers

TitleStatusHype
iMedImage Technical Report—0
AssistPDA: An Online Video Surveillance Assistant for Video Anomaly Prediction, Detection, and Analysis—0
VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic FaithfulnessCode5
Omni-AD: Learning to Reconstruct Global and Local Features for Multi-class Anomaly DetectionCode1
VADMamba: Exploring State Space Models for Fast Video Anomaly DetectionCode1
The MVTec AD 2 Dataset: Advanced Scenarios for Unsupervised Anomaly DetectionCode1
Adaptive State-Space Mamba for Real-Time Sensor Data Anomaly Detection—0
Channel impulse response peak clustering using neural networks—0
Refining Time Series Anomaly Detectors using Large Language Models—0
LogicQA: Logical Anomaly Detection with Vision Language Model Generated Questions—0
β-GNN: A Robust Ensemble Approach Against Graph Structure PerturbationCode0
Post-Hoc Calibrated Anomaly Detection—0
Social Network User Profiling for Anomaly Detection Based on Graph Neural Networks—0
Correcting Deviations from Normality: A Reformulated Diffusion Model for Multi-Class Unsupervised Anomaly DetectionCode2
Anomaly Detection Using Computer Vision: A Comparative Analysis of Class Distinction and Performance Metrics—0
Risk-Based Thresholding for Reliable Anomaly Detection in Concentrated Solar Power Plants—0
Towards Training-free Anomaly Detection with Vision and Language Foundation ModelsCode2
RoCA: Robust Contrastive One-class Time Series Anomaly Detection with Contaminated DataCode0
CRCL: Causal Representation Consistency Learning for Anomaly Detection in Surveillance Videos—0
Anomize: Better Open Vocabulary Video Anomaly Detection—0
GLADMamba: Unsupervised Graph-Level Anomaly Detection Powered by Selective State Space ModelCode0
Anomaly Detection and Localization for Speech Deepfakes via Feature Pyramid Matching—0
Assessing workflow impact and clinical utility of AI-assisted brain aneurysm detection: a multi-reader study—0
TRACE: Time SeRies PArameter EffiCient FinE-tuning—0
CausalRivers -- Scaling up benchmarking of causal discovery for real-world time-series—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