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 2211–2220 of 4856 papers

Title	Date	Tasks	Status	Hype
Seq-HyGAN: Sequence Classification via Hypergraph Attention Network	Mar 4, 2023	Anomaly DetectionClassification	—Unverified	0
Early Warning Signals of Social Instabilities in Twitter Data	Mar 3, 2023	Anomaly Detection	—Unverified	0
Unsupervised Recycled FPGA Detection Using Symmetry Analysis	Mar 3, 2023	Anomaly DetectionDensity Ratio Estimation	—Unverified	0
Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption	Mar 2, 2023	Anomaly DetectionDenoising	—Unverified	0
Navigating the Metric Maze: A Taxonomy of Evaluation Metrics for Anomaly Detection in Time Series	Mar 2, 2023	Anomaly DetectionTime Series	CodeCode Available	0
Multi-Task Self-Supervised Time-Series Representation Learning	Mar 2, 2023	Anomaly DetectionContrastive Learning	—Unverified	0
CADeSH: Collaborative Anomaly Detection for Smart Homes	Mar 2, 2023	Anomaly DetectionIntrusion Detection	—Unverified	0
Blockchain-enabled fraud discovery through abnormal smart contract detection on Ethereum	Mar 1, 2023	Anomaly Detection	—Unverified	0
Implementing Active Learning in Cybersecurity: Detecting Anomalies in Redacted Emails	Mar 1, 2023	Active LearningAnomaly Detection	—Unverified	0
Multimodal Industrial Anomaly Detection via Hybrid Fusion	Mar 1, 2023	3D Anomaly DetectionAnomaly Detection	CodeCode Available	2

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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