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 4251–4260 of 4856 papers

Title	Date	Tasks	Status
Sequential online prediction in the presence of outliers and change points: an instant temporal structure learning approach	Jul 15, 2019	Anomaly Detection	—Unverified
ALFA: A Dataset for UAV Fault and Anomaly Detection	Jul 14, 2019	Anomaly DetectionFault Detection	CodeCode Available
Towards Robust Voice Pathology Detection	Jul 13, 2019	Anomaly DetectionVoice pathology detection	—Unverified
AMAD: Adversarial Multiscale Anomaly Detection on High-Dimensional and Time-Evolving Categorical Data	Jul 12, 2019	Anomaly Detection	—Unverified
Automated Real-time Anomaly Detection in Human Trajectories using Sequence to Sequence Networks	Jul 12, 2019	Anomaly DetectionTime Series	—Unverified
FortuneTeller: Predicting Microarchitectural Attacks via Unsupervised Deep Learning	Jul 8, 2019	Anomaly DetectionDeep Learning	—Unverified
Statistical Analysis of Nearest Neighbor Methods for Anomaly Detection	Jul 8, 2019	Anomaly Detection	CodeCode Available
Interpretable Feature Learning in Multivariate Big Data Analysis for Network Monitoring	Jul 5, 2019	Anomaly Detection	—Unverified
VELC: A New Variational AutoEncoder Based Model for Time Series Anomaly Detection	Jul 3, 2019	Anomaly DetectionDecoder	CodeCode Available
Unsupervised Anomalous Trajectory Detection for Crowded Scenes	Jul 3, 2019	Anomaly DetectionClustering	—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