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 3881–3890 of 4856 papers

Title	Date	Tasks	Status
On Multi-Session Website Fingerprinting over TLS Handshake	Sep 19, 2020	Anomaly DetectionRecommendation Systems	—Unverified
PANDA: Predicting the change in proteins binding affinity upon mutations using sequence information	Sep 16, 2020	Anomaly Detection	CodeCode Available
Anomaly and Fraud Detection in Credit Card Transactions Using the ARIMA Model	Sep 16, 2020	Anomaly DetectionFraud Detection	—Unverified
Machine Learning and Data Science approach towards trend and predictors analysis of CDC Mortality Data for the USA	Sep 11, 2020	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Machine Learning Applications in Misuse and Anomaly Detection	Sep 10, 2020	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Label-Free Segmentation of COVID-19 Lesions in Lung CT	Sep 8, 2020	Anomaly DetectionCOVID-19 Diagnosis	—Unverified
Detection of Anomalies and Faults in Industrial IoT Systems by Data Mining: Study of CHRIST Osmotron Water Purification System	Sep 8, 2020	Anomaly DetectionFault Detection	—Unverified
Anomaly Detection With Partitioning Overfitting Autoencoder Ensembles	Sep 6, 2020	Anomaly DetectionOutlier Detection	CodeCode Available
Deep Learning for the Analysis of Disruption Precursors based on Plasma Tomography	Sep 6, 2020	Anomaly Detection	—Unverified
Towards the Development of Entropy-Based Anomaly Detection in an Astrophysics Simulation	Sep 5, 2020	Anomaly DetectionBIG-bench Machine Learning	—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	GLASS	Detection AUROC	99.9	—	Unverified
5	UniNet	Detection AUROC	99.9	—	Unverified
6	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
7	DDAD	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	PBAS	Detection AUROC	99.8	—	Unverified
10	HETMM	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	DDAD	Detection AUROC	98.9	—	Unverified
5	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
7	DiffusionAD	Detection AUROC	98.8	—	Unverified
8	GLASS	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