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 3431–3440 of 4856 papers

Title	Date	Tasks	Status	Hype
Hop-Count Based Self-Supervised Anomaly Detection on Attributed Networks	Apr 16, 2021	Anomaly DetectionSelf-Supervised Anomaly Detection	CodeCode Available	0
Holmes: An Efficient and Lightweight Semantic Based Anomalous Email Detector	Apr 16, 2021	Anomaly DetectionNovelty Detection	—Unverified	0
OneLog: Towards End-to-End Training in Software Log Anomaly Detection	Apr 15, 2021	Anomaly Detection	—Unverified	0
An ADMM-based Optimal Transmission Frequency Management System for IoT Edge Intelligence	Apr 15, 2021	Anomaly DetectionManagement	—Unverified	0
Weakly Supervised Video Anomaly Detection via Center-guided Discriminative Learning	Apr 15, 2021	Anomaly DetectionAnomaly Detection In Surveillance Videos	CodeCode Available	1
A Vision-based System for Traffic Anomaly Detection using Deep Learning and Decision Trees	Apr 14, 2021	Anomaly Detection	—Unverified	0
Context-Dependent Anomaly Detection for Low Altitude Traffic Surveillance	Apr 14, 2021	Anomaly DetectionContextual Anomaly Detection	—Unverified	0
Learning Normal Dynamics in Videos with Meta Prototype Network	Apr 14, 2021	Anomaly DetectionMeta-Learning	CodeCode Available	1
Global Information Guided Video Anomaly Detection	Apr 14, 2021	Anomaly DetectionSpatial Reasoning	—Unverified	0
ADNet: Temporal Anomaly Detection in Surveillance Videos	Apr 14, 2021	Anomaly DetectionAnomaly Detection In Surveillance Videos	CodeCode Available	1

Show:10 25 50

← PrevPage 344 of 486Next →

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	UniNet	Detection AUROC	99.9	—	Unverified
5	GLASS	Detection AUROC	99.9	—	Unverified
6	PBAS	Detection AUROC	99.8	—	Unverified
7	HETMM	Detection AUROC	99.8	—	Unverified
8	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	EfficientAD (early stopping)	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	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	DDAD	Detection AUROC	98.9	—	Unverified
7	GLASS	Detection AUROC	98.8	—	Unverified
8	DiffusionAD	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