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 2541–2550 of 4856 papers

Title	Date	Tasks	Status
No Free Lunch: The Hazards of Over-Expressive Representations in Anomaly Detection	Jun 12, 2023	Anomaly Detection	—Unverified
Coupled Attention Networks for Multivariate Time Series Anomaly Detection	Jun 12, 2023	Anomaly DetectionDecoder	—Unverified
Improving Time Series Encoding with Noise-Aware Self-Supervised Learning and an Efficient Encoder	Jun 11, 2023	Anomaly DetectionContrastive Learning	CodeCode Available
QBSD: Quartile-Based Seasonality Decomposition for Cost-Effective RAN KPI Forecasting	Jun 9, 2023	Anomaly DetectionTime Series	CodeCode Available
Log-based Anomaly Detection based on EVT Theory with feedback	Jun 8, 2023	Anomaly Detection	—Unverified
G^2uardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clustering	Jun 8, 2023	Anomaly DetectionClustering	—Unverified
IsoEx: an explainable unsupervised approach to process event logs cyber investigation	Jun 7, 2023	Anomaly DetectionExplainable artificial intelligence	—Unverified
Permutation invariant Gaussian matrix models for financial correlation matrices	Jun 7, 2023	Anomaly Detection	—Unverified
A Functional Data Perspective and Baseline On Multi-Layer Out-of-Distribution Detection	Jun 6, 2023	Anomaly DetectionOut-of-Distribution Detection	CodeCode Available
High-dimensional and Permutation Invariant Anomaly Detection	Jun 6, 2023	Anomaly DetectionDensity Estimation	CodeCode Available

Show:10 25 50

← PrevPage 255 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	UniNet	Detection AUROC	99.9	—	Unverified
5	GLASS	Detection AUROC	99.9	—	Unverified
6	PBAS	Detection AUROC	99.8	—	Unverified
7	HETMM	Detection AUROC	99.8	—	Unverified
8	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	EfficientAD (early stopping)	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	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	DDAD	Detection AUROC	98.9	—	Unverified
7	GLASS	Detection AUROC	98.8	—	Unverified
8	DiffusionAD	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