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 1401–1410 of 4856 papers

Title	Date	Tasks	Status	Hype
Structural Teacher-Student Normality Learning for Multi-Class Anomaly Detection and Localization	Feb 27, 2024	Anomaly DetectionKnowledge Distillation	—Unverified	0
CGGM: A conditional graph generation model with adaptive sparsity for node anomaly detection in IoT networks	Feb 27, 2024	Anomaly DetectionGraph Generation	—Unverified	0
Making Anomalies More Anomalous: Video Anomaly Detection Using a Novel Generator and Destroyer	Feb 26, 2024	Anomaly DetectionVideo Anomaly Detection	CodeCode Available	0
TOTEM: TOkenized Time Series EMbeddings for General Time Series Analysis	Feb 26, 2024	Anomaly DetectionImputation	CodeCode Available	3
Uncertainty Quantification in Anomaly Detection with Cross-Conformal p-Values	Feb 26, 2024	Anomaly DetectionConformal Prediction	CodeCode Available	0
Towards Fair Graph Anomaly Detection: Problem, Benchmark Datasets, and Evaluation	Feb 25, 2024	Anomaly DetectionFairness	CodeCode Available	1
Attention-GAN for Anomaly Detection: A Cutting-Edge Approach to Cybersecurity Threat Management	Feb 25, 2024	Anomaly DetectionData Augmentation	—Unverified	0
PatchFlow: Leveraging a Flow-Based Model with Patch Features	Feb 24, 2024	Anomaly DetectionDefect Detection	—Unverified	0
PUAD: Frustratingly Simple Method for Robust Anomaly Detection	Feb 23, 2024	Anomaly DetectionOut-of-Distribution Detection	CodeCode Available	1
Low-Rank Representations Meets Deep Unfolding: A Generalized and Interpretable Network for Hyperspectral Anomaly Detection	Feb 23, 2024	Anomaly Detection	—Unverified	0

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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	HETMM	Detection AUROC	99.8	—	Unverified
7	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	PBAS	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	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	DDAD	Detection AUROC	98.9	—	Unverified
6	Dinomaly ViT-L (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