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 611–620 of 4856 papers

Title	Date	Tasks	Status	Hype
Hyperedge Anomaly Detection with Hypergraph Neural Network	Dec 7, 2024	Anomaly DetectionLink Prediction	CodeCode Available	0
Leveraging Time-Series Foundation Model for Subsurface Well Logs Prediction and Anomaly Detection	Dec 7, 2024	Anomaly DetectionComputational Efficiency	—Unverified	0
A New Perspective on Time Series Anomaly Detection: Faster Patch-based Broad Learning System	Dec 7, 2024	Anomaly DetectionContrastive Learning	—Unverified	0
Self-Supervised Masked Mesh Learning for Unsupervised Anomaly Detection on 3D Cortical Surfaces	Dec 7, 2024	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified	0
COOOL: Challenge Of Out-Of-Label A Novel Benchmark for Autonomous Driving	Dec 6, 2024	Anomaly DetectionAutonomous Driving	CodeCode Available	1
Anomaly Detection and Classification in Knowledge Graphs	Dec 6, 2024	Anomaly DetectionClassification	—Unverified	0
NLP-ADBench: NLP Anomaly Detection Benchmark	Dec 6, 2024	Anomaly DetectionFraud Detection	CodeCode Available	1
MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects	Dec 6, 2024	2kAnomaly Detection	—Unverified	0
ETLNet: An Efficient TCN-BiLSTM Network for Road Anomaly Detection Using Smartphone Sensors	Dec 6, 2024	Anomaly Detection	—Unverified	0
Revitalizing Reconstruction Models for Multi-class Anomaly Detection via Class-Aware Contrastive Learning	Dec 6, 2024	Anomaly DetectionContrastive Learning	—Unverified	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