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 591–600 of 4856 papers

Title	Date	Tasks	Status	Hype
CODiT: Conformal Out-of-Distribution Detection in Time-Series Data	Jul 24, 2022	Anomaly DetectionAutonomous Driving	CodeCode Available	1
Dynamic Local Aggregation Network with Adaptive Clusterer for Anomaly Detection	Jul 22, 2022	Anomaly DetectionDiversity	CodeCode Available	1
Video Anomaly Detection by Solving Decoupled Spatio-Temporal Jigsaw Puzzles	Jul 20, 2022	Anomaly DetectionSelf-Supervised Learning	CodeCode Available	1
Informative knowledge distillation for image anomaly segmentation	Jul 19, 2022	Anomaly DetectionAnomaly Segmentation	CodeCode Available	1
Anomal-E: A Self-Supervised Network Intrusion Detection System based on Graph Neural Networks	Jul 14, 2022	Anomaly DetectionIntrusion Detection	CodeCode Available	1
Deep Learning for Anomaly Detection in Log Data: A Survey	Jul 8, 2022	Anomaly DetectionDeep Learning	CodeCode Available	1
DenseHybrid: Hybrid Anomaly Detection for Dense Open-set Recognition	Jul 6, 2022	Anomaly DetectionOpen Set Learning	CodeCode Available	1
Leveraging Trajectory Prediction for Pedestrian Video Anomaly Detection	Jul 5, 2022	Anomaly DetectionEvent Detection	CodeCode Available	1
Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detection	Jul 4, 2022	Anomaly DetectionContrastive Learning	CodeCode Available	1
Deep Contrastive One-Class Time Series Anomaly Detection	Jul 4, 2022	Anomaly DetectionContrastive 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	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