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 1481–1490 of 4856 papers

Title	Date	Tasks	Status
Deep Learning-based Anomaly Detection and Log Analysis for Computer Networks	Jul 8, 2024	Anomaly DetectionGenerative Adversarial Network	—Unverified
Deep Learning-Based Anomaly Detection in Synthetic Aperture Radar Imaging	Oct 28, 2022	Anomaly DetectionChange Detection	—Unverified
Deep Learning-based Anomaly Detection on X-ray Images of Fuel Cell Electrodes	Feb 15, 2022	Anomaly DetectionBinary Classification	—Unverified
Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses	Apr 5, 2021	Anomaly DetectionAutonomous Driving	—Unverified
Decentralized Federated Anomaly Detection in Smart Grids: A P2P Gossip Approach	Jul 20, 2024	Anomaly DetectionFederated Learning	—Unverified
Deep Learning-based ECG Classification on Raspberry PI using a Tensorflow Lite Model based on PTB-XL Dataset	Aug 25, 2022	Anomaly DetectionECG Classification	—Unverified
Deep Learning-Driven Anomaly Detection for Green IoT Edge Networks	Mar 1, 2024	Anomaly DetectionDeep Learning	—Unverified
Deep Learning for Anomaly Detection: A Review	Jul 6, 2020	Anomaly DetectionDeep Learning	—Unverified
ANVIL: Anomaly-based Vulnerability Identification without Labelled Training Data	Aug 28, 2024	Anomaly DetectionCode Generation	—Unverified
An On-Device Federated Learning Approach for Cooperative Model Update between Edge Devices	Feb 27, 2020	Anomaly DetectionFederated Learning	—Unverified

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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