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 1671–1680 of 4856 papers

Title	Date	Tasks	Status	Score
Coupled-Space Attacks against Random-Walk-based Anomaly Detection	Jul 26, 2023	Anomaly DetectionGraph Anomaly Detection	CodeCode Available	5
Efficient Model Monitoring for Quality Control in Cardiac Image Segmentation	Apr 12, 2021	Anomaly DetectionCardiac Segmentation	CodeCode Available	5
SIFT and SURF based feature extraction for the anomaly detection	Mar 24, 2022	Anomaly DetectionOne-class classifier	CodeCode Available	5
DINAMO: Dynamic and INterpretable Anomaly MOnitoring for Large-Scale Particle Physics Experiments	Jan 31, 2025	Anomaly Detection	CodeCode Available	5
Enhancing Wrist Fracture Detection with YOLO	Jul 17, 2024	Anomaly DetectionFracture detection	CodeCode Available	5
Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated Learning	Feb 8, 2025	Anomaly DetectionFederated Learning	CodeCode Available	5
Blind Image Denoising and Inpainting Using Robust Hadamard Autoencoders	Jan 26, 2021	Anomaly DetectionDenoising	CodeCode Available	5
Discovering Antagonists in Networks of Systems: Robot Deployment	Feb 27, 2025	Anomaly DetectionContextual Anomaly Detection	CodeCode Available	5
DTOR: Decision Tree Outlier Regressor to explain anomalies	Mar 16, 2024	Anomaly Detectionvalid	CodeCode Available	5
BiPOCO: Bi-Directional Trajectory Prediction with Pose Constraints for Pedestrian Anomaly Detection	Jul 5, 2022	Anomaly DetectionAutonomous Driving	CodeCode Available	5

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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