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 1701–1710 of 4856 papers

Title	Date	Tasks	Status	Hype
VALD-GAN: video anomaly detection using latent discriminator augmented GAN	Oct 18, 2023	Anomaly DetectionAnomaly Detection In Surveillance Videos	—Unverified	0
Open-Set Multivariate Time-Series Anomaly Detection	Oct 18, 2023	Anomaly DetectionContrastive Learning	—Unverified	0
A Multi-Scale Decomposition MLP-Mixer for Time Series Analysis	Oct 18, 2023	Anomaly DetectionImputation	CodeCode Available	1
PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly Detection	Oct 18, 2023	Anomaly DetectionContrastive Learning	CodeCode Available	1
Data Drift Monitoring for Log Anomaly Detection Pipelines	Oct 17, 2023	Anomaly DetectionDrift Detection	—Unverified	0
MST-GAT: A Multimodal Spatial-Temporal Graph Attention Network for Time Series Anomaly Detection	Oct 17, 2023	Anomaly DetectionGraph Attention	—Unverified	0
Leveraging Large Language Model for Automatic Evolving of Industrial Data-Centric R&D Cycle	Oct 17, 2023	Anomaly DetectionDecision Making	—Unverified	0
Spatially-resolved hyperlocal weather prediction and anomaly detection using IoT sensor networks and machine learning techniques	Oct 17, 2023	Anomaly DetectionDecision Making	—Unverified	0
Transparent Anomaly Detection via Concept-based Explanations	Oct 16, 2023	Anomaly DetectionClassification	—Unverified	0
TRANSOM: An Efficient Fault-Tolerant System for Training LLMs	Oct 16, 2023	Anomaly DetectionGPU	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