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 1851–1860 of 4856 papers

Title	Date	Tasks	Status	Hype
Semi-Supervised Anomaly Detection for the Determination of Vehicle Hijacking Tweets	Aug 19, 2023	Anomaly DetectionSemi-supervised Anomaly Detection	—Unverified	0
Voucher Abuse Detection with Prompt-based Fine-tuning on Graph Neural Networks	Aug 19, 2023	Abuse DetectionAnomaly Detection	—Unverified	0
Practical Anomaly Detection over Multivariate Monitoring Metrics for Online Services	Aug 19, 2023	Anomaly Detection	—Unverified	0
Anomaly-Aware Semantic Segmentation via Style-Aligned OoD Augmentation	Aug 19, 2023	Anomaly DetectionAnomaly Segmentation	—Unverified	0
CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly Detection	Aug 18, 2023	Anomaly DetectionContrastive Learning	CodeCode Available	1
Online Transition-Based Feature Generation for Anomaly Detection in Concurrent Data Streams	Aug 17, 2023	Anomaly DetectionComputational Efficiency	—Unverified	0
On the Effectiveness of Log Representation for Log-based Anomaly Detection	Aug 17, 2023	Anomaly DetectionLog Parsing	CodeCode Available	1
Forensic Data Analytics for Anomaly Detection in Evolving Networks	Aug 17, 2023	Anomaly DetectionFeature Engineering	—Unverified	0
Beyond Sharing: Conflict-Aware Multivariate Time Series Anomaly Detection	Aug 17, 2023	Anomaly DetectionMixture-of-Experts	CodeCode Available	0
Detecting Financial Market Manipulation with Statistical Physics Tools	Aug 16, 2023	Anomaly Detection	—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