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 2341–2350 of 4856 papers

Title	Date	Tasks	Status	Hype
Exploring the Use of Data-Driven Approaches for Anomaly Detection in the Internet of Things (IoT) Environment	Dec 31, 2022	Anomaly DetectionDeep Learning	—Unverified	0
Application Of ADNN For Background Subtraction In Smart Surveillance System	Dec 31, 2022	Anomaly DetectionMotion Detection	—Unverified	0
Skeletal Video Anomaly Detection using Deep Learning: Survey, Challenges and Future Directions	Dec 31, 2022	Anomaly DetectionVideo Anomaly Detection	—Unverified	0
Time series Forecasting to detect anomalous behaviours in Multiphase Flow Meters	Dec 30, 2022	Anomaly DetectionTime Series	—Unverified	0
Label-Efficient Interactive Time-Series Anomaly Detection	Dec 30, 2022	Active LearningAnomaly Detection	—Unverified	0
PRISM: Privacy Preserving Healthcare Internet of Things Security Management	Dec 27, 2022	Anomaly DetectionEdge-computing	—Unverified	0
AER: Auto-Encoder with Regression for Time Series Anomaly Detection	Dec 27, 2022	Anomaly DetectionBenchmarking	CodeCode Available	3
Anomaly detection in laser-guided vehicles' batteries: a case study	Dec 27, 2022	Anomaly DetectionScheduling	—Unverified	0
Application of Unsupervised Domain Adaptation for Structural MRI Analysis	Dec 26, 2022	Anomaly DetectionBinary Classification	—Unverified	0
A Novel Self-Supervised Learning-Based Anomaly Node Detection Method Based on an Autoencoder in Wireless Sensor Networks	Dec 26, 2022	Anomaly DetectionGraph Neural Network	CodeCode Available	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