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 2931–2940 of 4856 papers

Title	Date	Tasks	Status	Hype
Deep Graph Learning for Anomalous Citation Detection	Feb 23, 2022	Anomaly DetectionCPU	—Unverified	0
ML-based Anomaly Detection in Optical Fiber Monitoring	Feb 23, 2022	Anomaly Detection	—Unverified	0
Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptors	Feb 23, 2022	3D Anomaly Detection3D Anomaly Detection and Segmentation	—Unverified	0
Beyond Dents and Scratches: Logical Constraints in Unsupervised Anomaly Detection and Localization	Feb 22, 2022	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified	0
ALGAN: Anomaly Detection by Generating Pseudo Anomalous Data via Latent Variables	Feb 21, 2022	Anomaly DetectionGenerative Adversarial Network	—Unverified	0
Recurrent Auto-Encoder With Multi-Resolution Ensemble and Predictive Coding for Multivariate Time-Series Anomaly Detection	Feb 21, 2022	Anomaly DetectionDecoder	—Unverified	0
ICSML: Industrial Control Systems ML Framework for native inference using IEC 61131-3 code	Feb 21, 2022	Anomaly Detection	CodeCode Available	1
Energy-Efficient Respiratory Anomaly Detection in Premature Newborn Infants	Feb 21, 2022	Anomaly DetectionModel Selection	—Unverified	0
A Novel Anomaly Detection Method for Multimodal WSN Data Flow via a Dynamic Graph Neural Network	Feb 19, 2022	Anomaly DetectionGraph Neural Network	—Unverified	0
Anomalib: A Deep Learning Library for Anomaly Detection	Feb 16, 2022	Anomaly DetectionDeep Learning	—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	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