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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 2681–2690 of 4856 papers

Title	Date	Tasks	Status
Understanding the Challenges and Opportunities of Pose-based Anomaly Detection	Mar 9, 2023	Anomaly DetectionPose-based Anomaly Detection	—Unverified
Region and Spatial Aware Anomaly Detection for Fundus Images	Mar 7, 2023	Anomaly Detection	—Unverified
TinyAD: Memory-efficient anomaly detection for time series data in Industrial IoT	Mar 7, 2023	Anomaly DetectionTime Series	—Unverified
Time series anomaly detection with reconstruction-based state-space models	Mar 6, 2023	Anomaly DetectionDecoder	CodeCode Available
Memory Maps for Video Object Detection and Tracking on UAVs	Mar 6, 2023	Anomaly DetectionMulti-Object Tracking	—Unverified
PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flow	Mar 5, 2023	Anomaly DetectionVocal Bursts Intensity Prediction	—Unverified
Achieving Counterfactual Fairness for Anomaly Detection	Mar 4, 2023	Anomaly Detectioncounterfactual	CodeCode Available
Seq-HyGAN: Sequence Classification via Hypergraph Attention Network	Mar 4, 2023	Anomaly DetectionClassification	—Unverified
Unsupervised Recycled FPGA Detection Using Symmetry Analysis	Mar 3, 2023	Anomaly DetectionDensity Ratio Estimation	—Unverified
Early Warning Signals of Social Instabilities in Twitter Data	Mar 3, 2023	Anomaly Detection	—Unverified

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All datasets MVTec AD VisA MVTec LOCO AD One-class CIFAR-10 CUHK Avenue ShanghaiTech Fishyscapes L&F UCR Anomaly Archive MPDD BTAD One-class CIFAR-100 UBnormal

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	CPR-faster(TensorRT)	FPS	1,016	—	Unverified
2	CPR-fast(TensorRT)	FPS	362	—	Unverified
3	CPR(TensorRT)	FPS	130	—	Unverified
4	UniNet	Detection AUROC	99.9	—	Unverified
5	GLASS	Detection AUROC	99.9	—	Unverified
6	PBAS	Detection AUROC	99.8	—	Unverified
7	HETMM	Detection AUROC	99.8	—	Unverified
8	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	UniNet	Detection AUROC	99.8	—	Unverified
2	GLAD	Detection AUROC	99.5	—	Unverified
3	UniNet(model-unified multi-class)	Detection AUROC	99.15	—	Unverified
4	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	DDAD	Detection AUROC	98.9	—	Unverified
7	GLASS	Detection AUROC	98.8	—	Unverified
8	DiffusionAD	Detection AUROC	98.8	—	Unverified
9	TransFusion	Detection AUROC	98.7	—	Unverified
10	HETMM	Detection AUROC	98.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CSAD	Avg. Detection AUROC	95.3	—	Unverified
2	PSAD	Avg. Detection AUROC	94.9	—	Unverified