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 4051–4060 of 4856 papers

Title	Date	Tasks	Status
Real-time Out-of-distribution Detection in Learning-Enabled Cyber-Physical Systems	Jan 28, 2020	Anomaly DetectionBIG-bench Machine Learning	—Unverified
A clustering approach to time series forecasting using neural networks: A comparative study on distance-based vs. feature-based clustering methods	Jan 27, 2020	Anomaly DetectionClustering	CodeCode Available
Detection of Thin Boundaries between Different Types of Anomalies in Outlier Detection using Enhanced Neural Networks	Jan 24, 2020	Anomaly DetectionOutlier Detection	—Unverified
Learning a distance function with a Siamese network to localize anomalies in videos	Jan 24, 2020	Anomaly Detection	—Unverified
RePAD: Real-time Proactive Anomaly Detection for Time Series	Jan 24, 2020	Anomaly DetectionFraud Detection	—Unverified
Simple and Effective Prevention of Mode Collapse in Deep One-Class Classification	Jan 24, 2020	Anomaly DetectionGeneral Classification	—Unverified
Universal Data Anomaly Detection via Inverse Generative Adversary Network	Jan 23, 2020	Anomaly Detection	—Unverified
A versatile anomaly detection method for medical images with a flow-based generative model in semi-supervision setting	Jan 22, 2020	Anomaly Detection	—Unverified
An Intelligent and Time-Efficient DDoS Identification Framework for Real-Time Enterprise Networks SAD-F: Spark Based Anomaly Detection Framework	Jan 21, 2020	Anomaly Detectionfeature selection	—Unverified
Regularized Cycle Consistent Generative Adversarial Network for Anomaly Detection	Jan 18, 2020	Anomaly DetectionGenerative Adversarial Network	—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