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 431–440 of 4856 papers

Title	Date	Tasks	Status	Hype
ChatGPT for Digital Forensic Investigation: The Good, The Bad, and The Unknown	Jul 10, 2023	Anomaly DetectionCode Generation	CodeCode Available	1
DyEdgeGAT: Dynamic Edge via Graph Attention for Early Fault Detection in IIoT Systems	Jul 7, 2023	Anomaly DetectionFault Detection	CodeCode Available	1
Unsupervised Video Anomaly Detection with Diffusion Models Conditioned on Compact Motion Representations	Jul 4, 2023	Anomaly DetectionVideo Anomaly Detection	CodeCode Available	1
Graph-level Anomaly Detection via Hierarchical Memory Networks	Jul 3, 2023	Anomaly Detection	CodeCode Available	1
ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection	Jul 3, 2023	Anomaly DetectionImputation	CodeCode Available	1
Graph Neural Networks based Log Anomaly Detection and Explanation	Jul 2, 2023	Anomaly DetectionGraph Neural Network	CodeCode Available	1
Precursor-of-Anomaly Detection for Irregular Time Series	Jun 27, 2023	Anomaly DetectionIrregular Time Series	CodeCode Available	1
Anomaly Detection with Score Distribution Discrimination	Jun 26, 2023	Anomaly Detection	CodeCode Available	1
Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detection	Jun 26, 2023	Anomaly DetectionAnomaly Detection In Surveillance Videos	CodeCode Available	1
Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectors	Jun 21, 2023	Anomaly DetectionDecoder	CodeCode Available	1

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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