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 2851–2860 of 4856 papers

Title	Date	Tasks	Status	Hype
syslrn: Learning What to Monitor for Efficient Anomaly Detection	Mar 29, 2022	Anomaly Detection	CodeCode Available	0
AnoDFDNet: A Deep Feature Difference Network for Anomaly Detection	Mar 29, 2022	Anomaly Detectionobject-detection	CodeCode Available	1
Radial Autoencoders for Enhanced Anomaly Detection	Mar 29, 2022	Anomaly DetectionClassification	—Unverified	0
Contextual Information Based Anomaly Detection for a Multi-Scene UAV Aerial Videos	Mar 29, 2022	Anomaly DetectionManagement	CodeCode Available	0
FlexFringe: Modeling Software Behavior by Learning Probabilistic Automata	Mar 28, 2022	Anomaly Detection	CodeCode Available	1
Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection	Mar 28, 2022	Anomaly DetectionSupervised Anomaly Detection	CodeCode Available	1
Semi-supervised anomaly detection algorithm based on KL divergence (SAD-KL)	Mar 28, 2022	Anomaly DetectionSemi-supervised Anomaly Detection	—Unverified	0
PAEDID: Patch Autoencoder Based Deep Image Decomposition For Pixel-level Defective Region Segmentation	Mar 28, 2022	Anomaly Detection	—Unverified	0
A multi-stream deep neural network with late fuzzy fusion for real-world anomaly detection	Mar 27, 2022	Abnormal Event Detection In VideoAnomaly Detection	—Unverified	0
Learning to Adapt to Unseen Abnormal Activities under Weak Supervision	Mar 25, 2022	Anomaly DetectionMeta-Learning	CodeCode Available	1

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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