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 1571–1580 of 4856 papers

Title	Date	Tasks	Status	Hype
Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIs	Dec 7, 2023	AnatomyAnomaly Detection	CodeCode Available	1
Intelligent Anomaly Detection for Lane Rendering Using Transformer with Self-Supervised Pre-Training and Customized Fine-Tuning	Dec 7, 2023	Anomaly Detection	—Unverified	0
How Low Can You Go? Surfacing Prototypical In-Distribution Samples for Unsupervised Anomaly Detection	Dec 6, 2023	Anomaly DetectionDefect Detection	—Unverified	0
Anomaly Detection for Scalable Task Grouping in Reinforcement Learning-based RAN Optimization	Dec 6, 2023	Anomaly Detection	—Unverified	0
Pseudo Replay-based Class Continual Learning for Online New Category Anomaly Detection in Advanced Manufacturing	Dec 5, 2023	Anomaly Detectionclass-incremental learning	—Unverified	0
A Unified Simulation Framework for Visual and Behavioral Fidelity in Crowd Analysis	Dec 5, 2023	Anomaly DetectionCrowd Counting	—Unverified	0
Semi-Supervised Health Index Monitoring with Feature Generation and Fusion	Dec 5, 2023	Anomaly DetectionDiversity	—Unverified	0
A Self-Commissioning Edge Computing Method for Data-Driven Anomaly Detection in Power Electronic Systems	Dec 5, 2023	Anomaly DetectionDiversity	—Unverified	0
MEMTO: Memory-guided Transformer for Multivariate Time Series Anomaly Detection	Dec 5, 2023	Anomaly DetectionTime Series	CodeCode Available	1
Learning Cortical Anomaly through Masked Encoding for Unsupervised Heterogeneity Mapping	Dec 5, 2023	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