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 1681–1690 of 4856 papers

Title	Date	Tasks	Status	Hype
OrionBench: Benchmarking Time Series Generative Models in the Service of the End-User	Oct 26, 2023	Anomaly DetectionBenchmarking	—Unverified	0
Detecting subtle cyberattacks on adaptive cruise control vehicles: A machine learning approach	Oct 26, 2023	Anomaly DetectionGenerative Adversarial Network	—Unverified	0
A Coarse-to-Fine Pseudo-Labeling (C2FPL) Framework for Unsupervised Video Anomaly Detection	Oct 26, 2023	Anomaly DetectionOne-Class Classification	CodeCode Available	1
Towards Self-Interpretable Graph-Level Anomaly Detection	Oct 25, 2023	Anomaly DetectionGraph Anomaly Detection	CodeCode Available	1
On Pixel-level Performance Assessment in Anomaly Detection	Oct 25, 2023	Anomaly Detection	—Unverified	0
GADY: Unsupervised Anomaly Detection on Dynamic Graphs	Oct 25, 2023	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified	0
One or Two Things We know about Concept Drift -- A Survey on Monitoring Evolving Environments	Oct 24, 2023	Anomaly DetectionDrift Detection	—Unverified	0
Localizing Anomalies in Critical Infrastructure using Model-Based Drift Explanations	Oct 24, 2023	Anomaly Detection	CodeCode Available	0
ADoPT: LiDAR Spoofing Attack Detection Based on Point-Level Temporal Consistency	Oct 23, 2023	Anomaly DetectionAutonomous Vehicles	—Unverified	0
CAD-DA: Controllable Anomaly Detection after Domain Adaptation by Statistical Inference	Oct 23, 2023	Anomaly DetectionDomain Adaptation	—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