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 551–560 of 4856 papers

Title	Date	Tasks	Status	Hype
Dive into Time-Series Anomaly Detection: A Decade Review	Dec 29, 2024	Anomaly DetectionTime Series	CodeCode Available	0
Frames and vertex-frequency representations in graph fractional Fourier domain	Dec 28, 2024	Anomaly DetectionComputational Efficiency	—Unverified	0
An Anomaly Detection System Based on Generative Classifiers for Controller Area Network	Dec 28, 2024	Anomaly DetectionBayesian Inference	—Unverified	0
STNMamba: Mamba-based Spatial-Temporal Normality Learning for Video Anomaly Detection	Dec 28, 2024	Anomaly DetectionMamba	—Unverified	0
Injecting Explainability and Lightweight Design into Weakly Supervised Video Anomaly Detection Systems	Dec 28, 2024	Anomaly DetectionBinary Classification	—Unverified	0
Graph Mixture of Experts and Memory-augmented Routers for Multivariate Time Series Anomaly Detection	Dec 26, 2024	Anomaly DetectionMixture-of-Experts	—Unverified	0
Time Series Foundational Models: Their Role in Anomaly Detection and Prediction	Dec 26, 2024	Anomaly DetectionPrediction	CodeCode Available	0
CausalTAD: Causal Implicit Generative Model for Debiased Online Trajectory Anomaly Detection	Dec 25, 2024	Anomaly Detection	—Unverified	0
Quo Vadis, Anomaly Detection? LLMs and VLMs in the Spotlight	Dec 24, 2024	Anomaly DetectionVideo Anomaly Detection	CodeCode Available	1
Cech Complex Generation with Homotopy Equivalence Framework for Myocardial Infarction Diagnosis using Electrocardiogram Signals	Dec 23, 2024	Anomaly DetectionRhythm	—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