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 4561–4570 of 4856 papers

Title	Date	Tasks	Status
A brief introduction to a framework named Multilevel Guidance-Exploration Network	Dec 7, 2023	Anomaly Detection	CodeCode Available
Online anomaly detection using statistical leverage for streaming business process events	Mar 1, 2021	Anomaly Detection	CodeCode Available
Deep Anomaly Detection under Labeling Budget Constraints	Feb 15, 2023	Anomaly DetectionFraud Detection	CodeCode Available
Decoupled Appearance and Motion Learning for Efficient Anomaly Detection in Surveillance Video	Nov 10, 2020	Anomaly Detection	CodeCode Available
Video Anomaly Detection by Estimating Likelihood of Representations	Dec 2, 2020	Action RecognitionAnomaly Detection	CodeCode Available
Online-compatible Unsupervised Non-resonant Anomaly Detection	Nov 11, 2021	Anomaly DetectionSensitivity	CodeCode Available
Online Cyber-Attack Detection in Smart Grid: A Reinforcement Learning Approach	Sep 14, 2018	Anomaly DetectionCyber Attack Detection	CodeCode Available
A MIL Approach for Anomaly Detection in Surveillance Videos from Multiple Camera Views	Jul 2, 2023	Anomaly DetectionAnomaly Detection In Surveillance Videos	CodeCode Available
Self-supervised Representation Learning for Reliable Robotic Monitoring of Fruit Anomalies	Sep 21, 2021	Anomaly DetectionData Augmentation	CodeCode Available
AdeNet: Deep learning architecture that identifies damaged electrical insulators in power lines	Mar 2, 2021	Anomaly DetectionTransfer Learning	CodeCode Available

Show:10 25 50

← PrevPage 457 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