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 4321–4330 of 4856 papers

Title	Date	Tasks	Status
Exploring Information Centrality for Intrusion Detection in Large Networks	Apr 27, 2019	Anomaly DetectionIntrusion Detection	—Unverified
Reducing Anomaly Detection in Images to Detection in Noise	Apr 25, 2019	Anomaly Detection	—Unverified
GAN Augmented Text Anomaly Detection with Sequences of Deep Statistics	Apr 24, 2019	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
A Comparison Study of Credit Card Fraud Detection: Supervised versus Unsupervised	Apr 24, 2019	Anomaly DetectionFraud Detection	—Unverified
Deep Representation Learning for Social Network Analysis	Apr 18, 2019	Anomaly DetectionAttribute	—Unverified
Deep Anomaly Detection for Generalized Face Anti-Spoofing	Apr 17, 2019	Anomaly DetectionFace Anti-Spoofing	CodeCode Available
Temporal Cycle-Consistency Learning	Apr 16, 2019	Anomaly DetectionRepresentation Learning	CodeCode Available
Graph-Based Method for Anomaly Prediction in Brain Network	Apr 15, 2019	Anomaly DetectionFunctional Connectivity	—Unverified
Should I Raise The Red Flag? A comprehensive survey of anomaly scoring methods toward mitigating false alarms	Apr 14, 2019	Anomaly DetectionIntrusion Detection	—Unverified
Adversarial Learning in Statistical Classification: A Comprehensive Review of Defenses Against Attacks	Apr 12, 2019	Anomaly DetectionData Poisoning	—Unverified

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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