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 1041–1050 of 4856 papers

Title	Date	Tasks	Status	Hype
Deep Learning-based Anomaly Detection and Log Analysis for Computer Networks	Jul 8, 2024	Anomaly DetectionGenerative Adversarial Network	—Unverified	0
Graph Anomaly Detection with Noisy Labels by Reinforcement Learning	Jul 8, 2024	Anomaly DetectionFraud Detection	—Unverified	0
CAV-AD: A Robust Framework for Detection of Anomalous Data and Malicious Sensors in CAV Networks	Jul 7, 2024	Anomaly Detection	—Unverified	0
SPINEX: Similarity-based Predictions with Explainable Neighbors Exploration for Anomaly and Outlier Detection	Jul 5, 2024	Anomaly DetectionDensity Estimation	—Unverified	0
Computer Vision for Clinical Gait Analysis: A Gait Abnormality Video Dataset	Jul 5, 2024	Action RecognitionAnomaly Detection	CodeCode Available	1
Machine Learning for Complex Systems with Abnormal Pattern by Exception Maximization Outlier Detection Method	Jul 5, 2024	Anomaly DetectionOutlier Detection	—Unverified	0
Feature Attenuation of Defective Representation Can Resolve Incomplete Masking on Anomaly Detection	Jul 5, 2024	Anomaly DetectionEdge-computing	CodeCode Available	0
Support Vector Based Anomaly Detection in Federated Learning	Jul 4, 2024	Anomaly DetectionFederated Learning	—Unverified	0
Looking for Tiny Defects via Forward-Backward Feature Transfer	Jul 4, 2024	Anomaly DetectionSegmentation	—Unverified	0
Seamless Monitoring of Stress Levels Leveraging a Universal Model for Time Sequences	Jul 4, 2024	Anomaly Detection	CodeCode Available	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