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 961–970 of 4856 papers

Title	Date	Tasks	Status
Latent Anomaly Detection: Masked VQ-GAN for Unsupervised Segmentation in Medical CBCT	Jun 17, 2025	Anomaly DetectionSegmentation	—Unverified
Bridging Unsupervised and Semi-Supervised Anomaly Detection: A Theoretically-Grounded and Practical Framework with Synthetic Anomalies	Jun 16, 2025	Anomaly DetectionSemi-supervised Anomaly Detection	—Unverified
Condition Monitoring with Machine Learning: A Data-Driven Framework for Quantifying Wind Turbine Energy Loss	Jun 16, 2025	Anomaly Detectionfeature selection	—Unverified
Polyra Swarms: A Shape-Based Approach to Machine Learning	Jun 16, 2025	Anomaly Detection	—Unverified
Hybrid Meta-Learning Framework for Anomaly Forecasting in Nonlinear Dynamical Systems via Physics-Inspired Simulation and Deep Ensembles	Jun 15, 2025	Anomaly DetectionAnomaly Forecasting	—Unverified
Temporal cross-validation impacts multivariate time series subsequence anomaly detection evaluation	Jun 13, 2025	Anomaly DetectionBenchmarking	—Unverified
Prioritizing Alignment Paradigms over Task-Specific Model Customization in Time-Series LLMs	Jun 13, 2025	Anomaly DetectionTime Series	CodeCode Available
Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective	Jun 13, 2025	Anomaly DetectionDimensionality Reduction	CodeCode Available
Data Driven Diagnosis for Large Cyber-Physical-Systems with Minimal Prior Information	Jun 12, 2025	Anomaly DetectionDiagnostic	—Unverified
IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain	Jun 12, 2025	Anomaly Detection	CodeCode Available

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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	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
7	DDAD	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	PBAS	Detection AUROC	99.8	—	Unverified
10	HETMM	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	DDAD	Detection AUROC	98.9	—	Unverified
5	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	INP-Former ViT-B (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