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 2461–2470 of 4856 papers

Title	Date	Tasks	Status
Benchmarking Jetson Edge Devices with an End-to-end Video-based Anomaly Detection System	Jul 28, 2023	Anomaly DetectionAutonomous Driving	CodeCode Available
BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection	Jul 28, 2023	Anomaly DetectionCoLA	—Unverified
Anomaly Detection in Industrial Machinery using IoT Devices and Machine Learning: a Systematic Mapping	Jul 28, 2023	Anomaly Detection	—Unverified
Robustness Verification of Deep Neural Networks using Star-Based Reachability Analysis with Variable-Length Time Series Input	Jul 26, 2023	Anomaly DetectionTime Series	CodeCode Available
Unraveling the Complexity of Splitting Sequential Data: Tackling Challenges in Video and Time Series Analysis	Jul 26, 2023	Anomaly DetectionObject Tracking	—Unverified
EasyNet: An Easy Network for 3D Industrial Anomaly Detection	Jul 26, 2023	3D Anomaly DetectionAnomaly Detection	—Unverified
Coupled-Space Attacks against Random-Walk-based Anomaly Detection	Jul 26, 2023	Anomaly DetectionGraph Anomaly Detection	CodeCode Available
ECG classification using Deep CNN and Gramian Angular Field	Jul 25, 2023	Anomaly DetectionClassification	—Unverified
Sobolev Space Regularised Pre Density Models	Jul 25, 2023	Anomaly DetectionDensity Estimation	—Unverified
Few-shot 1/a Anomalies Feedback : Damage Vision Mining Opportunity and Embedding Feature Imbalance	Jul 24, 2023	Anomaly Detectionimage-classification	—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	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