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 2841–2850 of 4856 papers

Title	Date	Tasks	Status
Quantifying point cloud realism through adversarially learned latent representations	Sep 24, 2021	Anomaly DetectionMetric Learning	—Unverified
Quantifying Sample Anonymity in Score-Based Generative Models with Adversarial Fingerprinting	Jun 2, 2023	Anomaly DetectionData Augmentation	—Unverified
Quantile LSTM: A Robust LSTM for Anomaly Detection In Time Series Data	Feb 17, 2023	Anomaly DetectionTime Series	—Unverified
Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology	Jun 24, 2025	Anomaly DetectionArtifact Detection	—Unverified
Quantized Non-Volatile Nanomagnetic Synapse based Autoencoder for Efficient Unsupervised Network Anomaly Detection	Sep 12, 2023	Anomaly Detection	—Unverified
Quantum Autoencoder for Multivariate Time Series Anomaly Detection	Apr 24, 2025	Anomaly DetectionData Compression	—Unverified
Quantum Hybrid Support Vector Machines for Stress Detection in Older Adults	Jan 8, 2025	Anomaly DetectionDiagnostic	—Unverified
Quantum-Hybrid Support Vector Machines for Anomaly Detection in Industrial Control Systems	Jun 21, 2025	Anomaly Detection	—Unverified
Quantum Machine Learning for Anomaly Detection in Consumer Electronics	Aug 30, 2024	Anomaly DetectionQuantum Machine Learning	—Unverified
Quantum Machine Learning in Log-based Anomaly Detection: Challenges and Opportunities	Dec 18, 2024	Anomaly DetectionModel Selection	—Unverified

Show:10 25 50

← PrevPage 285 of 486Next →

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