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 1931–1940 of 4856 papers

Title	Date	Tasks	Status	Hype
Matrix Profile based Anomaly Detection in Streaming Gait Data for Fall Prevention	Jul 18, 2023	Anomaly Detection	—Unverified	0
You've Got Two Teachers: Co-evolutionary Image and Report Distillation for Semi-supervised Anatomical Abnormality Detection in Chest X-ray	Jul 18, 2023	Anomaly DetectionPseudo Label	—Unverified	0
Anomaly Detection with Selective Dictionary Learning	Jul 17, 2023	Anomaly DetectionDictionary Learning	CodeCode Available	0
Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detection	Jul 17, 2023	Anomaly DetectionGraph Learning	CodeCode Available	1
LafitE: Latent Diffusion Model with Feature Editing for Unsupervised Multi-class Anomaly Detection	Jul 16, 2023	Anomaly DetectionMulti-class Anomaly Detection	—Unverified	0
Custom DNN using Reward Modulated Inverted STDP Learning for Temporal Pattern Recognition	Jul 15, 2023	Anomaly DetectionKeyword Spotting	—Unverified	0
Anomaly Detection in Automated Fibre Placement: Learning with Data Limitations	Jul 15, 2023	Anomaly DetectionBinary Classification	—Unverified	0
Masked Autoencoders for Unsupervised Anomaly Detection in Medical Images	Jul 14, 2023	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	1
Multimodal Motion Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection	Jul 14, 2023	2D Human Pose EstimationAnomaly Detection	CodeCode Available	1
cOOpD: Reformulating COPD classification on chest CT scans as anomaly detection using contrastive representations	Jul 14, 2023	Anomaly DetectionBinary Classification	—Unverified	0

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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	GLASS	Detection AUROC	99.9	—	Unverified
5	UniNet	Detection AUROC	99.9	—	Unverified
6	HETMM	Detection AUROC	99.8	—	Unverified
7	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	PBAS	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	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	DDAD	Detection AUROC	98.9	—	Unverified
6	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
7	DiffusionAD	Detection AUROC	98.8	—	Unverified
8	GLASS	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