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 201–225 of 4856 papers

TitleStatusHype
Noise-Resilient Point-wise Anomaly Detection in Time Series Using Weak Segment LabelsCode1
A Survey of World Models for Autonomous DrivingCode1
Exploring Pose-Based Anomaly Detection for Retail Security: A Real-World Shoplifting Dataset and BenchmarkCode1
HSTforU: anomaly detection in aerial and ground-based videos with hierarchical spatio-temporal transformer for U-netCode1
Quo Vadis, Anomaly Detection? LLMs and VLMs in the SpotlightCode1
VarAD: Lightweight High-Resolution Image Anomaly Detection via Visual Autoregressive ModelingCode1
Kernel-Aware Graph Prompt Learning for Few-Shot Anomaly DetectionCode1
Progressive Boundary Guided Anomaly Synthesis for Industrial Anomaly DetectionCode1
CwA-T: A Channelwise AutoEncoder with Transformer for EEG Abnormality DetectionCode1
Do Language Models Understand Time?Code1
Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly DetectionCode1
Boosting Fine-Grained Visual Anomaly Detection with Coarse-Knowledge-Aware Adversarial LearningCode1
AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and LocalizationCode1
AD-LLM: Benchmarking Large Language Models for Anomaly DetectionCode1
Filter or Compensate: Towards Invariant Representation from Distribution Shift for Anomaly DetectionCode1
Video Anomaly Detection with Motion and Appearance Guided Patch Diffusion ModelCode1
Federated Foundation Models on Heterogeneous Time SeriesCode1
Unlocking the Potential of Reverse Distillation for Anomaly DetectionCode1
COOOL: Challenge Of Out-Of-Label A Novel Benchmark for Autonomous DrivingCode1
NLP-ADBench: NLP Anomaly Detection BenchmarkCode1
Frequency-Guided Diffusion Model with Perturbation Training for Skeleton-Based Video Anomaly DetectionCode1
F-SE-LSTM: A Time Series Anomaly Detection Method with Frequency Domain InformationCode1
FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training DataCode1
TSINR: Capturing Temporal Continuity via Implicit Neural Representations for Time Series Anomaly DetectionCode1
UniGAD: Unifying Multi-level Graph Anomaly DetectionCode1
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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