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 4521–4530 of 4856 papers

Title	Date	Tasks	Status	Hype
Cardiac Motion Scoring with Segment- and Subject-level Non-Local Modeling	Jun 14, 2018	Anomaly Detection	—Unverified	0
Deep Generative Models in the Real-World: An Open Challenge from Medical Imaging	Jun 14, 2018	Anomaly Detection	—Unverified	0
Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection	Jun 13, 2018	Anomaly DetectionDisease Prediction	CodeCode Available	0
Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoders	Jun 13, 2018	Anomaly DetectionLesion Detection	CodeCode Available	0
Partial AUC Maximization via Nonlinear Scoring Functions	Jun 13, 2018	Anomaly DetectionBinary Classification	—Unverified	0
A Taxonomy of Network Threats and the Effect of Current Datasets on Intrusion Detection Systems	Jun 9, 2018	Anomaly DetectionIntrusion Detection	CodeCode Available	0
Learning Multi-Modal Self-Awareness Models for Autonomous Vehicles from Human Driving	Jun 7, 2018	Anomaly DetectionAutonomous Vehicles	—Unverified	0
A Multi-task Deep Learning Architecture for Maritime Surveillance using AIS Data Streams	Jun 6, 2018	Anomaly DetectionDeep Learning	CodeCode Available	0
Future Frame Prediction for Anomaly Detection â A New Baseline	Jun 1, 2018	Anomaly DetectionOptical Flow Estimation	CodeCode Available	1
Generative Modeling by Inclusive Neural Random Fields with Applications in Image Generation and Anomaly Detection	Jun 1, 2018	Anomaly DetectionImage Generation	CodeCode Available	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	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