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 401–410 of 4856 papers

Title	Date	Tasks	Status	Hype
Value Gradient Sampler: Sampling as Sequential Decision Making	Feb 18, 2025	Anomaly DetectionDecision Making	CodeCode Available	0
Statistically Significant kNNAD by Selective Inference	Feb 18, 2025	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified	0
A Survey of Anomaly Detection in Cyber-Physical Systems	Feb 18, 2025	Anomaly DetectionSurvey	—Unverified	0
Positional Encoding in Transformer-Based Time Series Models: A Survey	Feb 17, 2025	Anomaly DetectionBenchmarking	CodeCode Available	1
Unifying Explainable Anomaly Detection and Root Cause Analysis in Dynamical Systems	Feb 17, 2025	Anomaly Detection	—Unverified	0
Towards a Trustworthy Anomaly Detection for Critical Applications through Approximated Partial AUC Loss	Feb 17, 2025	Anomaly Detection	CodeCode Available	0
Component-aware Unsupervised Logical Anomaly Generation for Industrial Anomaly Detection	Feb 17, 2025	Anomaly Detection	—Unverified	0
DCAD-2000: A Multilingual Dataset across 2000+ Languages with Data Cleaning as Anomaly Detection	Feb 17, 2025	Anomaly Detection	CodeCode Available	0
WRT-SAM: Foundation Model-Driven Segmentation for Generalized Weld Radiographic Testing	Feb 17, 2025	Anomaly DetectionImage Segmentation	—Unverified	0
Enhanced Anomaly Detection in IoMT Networks using Ensemble AI Models on the CICIoMT2024 Dataset	Feb 17, 2025	Anomaly Detection	—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