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 3541–3550 of 4856 papers

Title	Date	Tasks	Status	Hype
On the Properties of Kullback-Leibler Divergence Between Multivariate Gaussian Distributions	Feb 10, 2021	Anomaly DetectionSafe Reinforcement Learning	—Unverified	0
Clustered Hierarchical Anomaly and Outlier Detection Algorithms	Feb 9, 2021	Anomaly DetectionClustering	CodeCode Available	1
Is Space-Time Attention All You Need for Video Understanding?	Feb 9, 2021	Action ClassificationAction Recognition	CodeCode Available	2
Negative Data Augmentation	Feb 9, 2021	Action RecognitionAnomaly Detection	CodeCode Available	1
VeeAlign: Multifaceted Context Representation using Dual Attention for Ontology Alignment	Feb 8, 2021	Anomaly DetectionData Integration	CodeCode Available	0
Exact Optimization of Conformal Predictors via Incremental and Decremental Learning	Feb 5, 2021	Anomaly Detectionregression	CodeCode Available	0
Graph Coding for Model Selection and Anomaly Detection in Gaussian Graphical Models	Feb 4, 2021	Anomaly DetectionModel Selection	—Unverified	0
SAFELearning: Enable Backdoor Detectability In Federated Learning With Secure Aggregation	Feb 4, 2021	Anomaly DetectionFederated Learning	—Unverified	0
Evaluation of Point Pattern Features for Anomaly Detection of Defect within Random Finite Set Framework	Feb 3, 2021	Anomaly DetectionDefect Detection	—Unverified	0
Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encoder	Feb 2, 2021	Anomaly DetectionDensity Estimation	—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	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