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
RAD: A Comprehensive Dataset for Benchmarking the Robustness of Image Anomaly DetectionCode1
LogiCode: an LLM-Driven Framework for Logical Anomaly DetectionCode1
Segment, Shuffle, and Stitch: A Simple Layer for Improving Time-Series RepresentationsCode1
Learning-Based Link Anomaly Detection in Continuous-Time Dynamic GraphsCode1
ARC: A Generalist Graph Anomaly Detector with In-Context LearningCode1
Incomplete Multimodal Industrial Anomaly Detection via Cross-Modal DistillationCode1
Spatial-aware Attention Generative Adversarial Network for Semi-supervised Anomaly Detection in Medical ImageCode1
PATE: Proximity-Aware Time series anomaly EvaluationCode1
Position-Guided Prompt Learning for Anomaly Detection in Chest X-RaysCode1
SimAD: A Simple Dissimilarity-based Approach for Time Series Anomaly DetectionCode1
Networking Systems for Video Anomaly Detection: A Tutorial and SurveyCode1
AnomalyLLM: Few-shot Anomaly Edge Detection for Dynamic Graphs using Large Language ModelsCode1
AnoVox: A Benchmark for Multimodal Anomaly Detection in Autonomous DrivingCode1
MRISegmentator-Abdomen: A Fully Automated Multi-Organ and Structure Segmentation Tool for T1-weighted Abdominal MRICode1
Supervised Anomaly Detection for Complex Industrial ImagesCode1
Position: Quo Vadis, Unsupervised Time Series Anomaly Detection?Code1
Advancing Pre-trained Teacher: Towards Robust Feature Discrepancy for Anomaly DetectionCode1
Dr-SAM: An End-to-End Framework for Vascular Segmentation, Diameter Estimation, and Anomaly Detection on Angiography ImagesCode1
Blind Localization and Clustering of Anomalies in TexturesCode1
DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time SeriesCode1
FastLogAD: Log Anomaly Detection with Mask-Guided Pseudo Anomaly Generation and DiscriminationCode1
SplatPose & Detect: Pose-Agnostic 3D Anomaly DetectionCode1
Dynamic Distinction Learning: Adaptive Pseudo Anomalies for Video Anomaly DetectionCode1
Collaborative Learning of Anomalies with Privacy (CLAP) for Unsupervised Video Anomaly Detection: A New BaselineCode1
Hierarchical Gaussian Mixture Normalizing Flow Modeling for Unified 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