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 2581–2590 of 4856 papers

Title	Date	Tasks	Status	Hype
A Comparative Study on Unsupervised Anomaly Detection for Time Series: Experiments and Analysis	Sep 10, 2022	Anomaly DetectionDiversity	—Unverified	0
Activity report analysis with automatic single or multispan answer extraction	Sep 9, 2022	Anomaly DetectionQuestion Answering	—Unverified	0
Fine-grain Inference on Out-of-Distribution Data with Hierarchical Classification	Sep 9, 2022	Anomaly DetectionOut of Distribution (OOD) Detection	—Unverified	0
Explanation Method for Anomaly Detection on Mixed Numerical and Categorical Spaces	Sep 9, 2022	Anomaly DetectionIntrusion Detection	—Unverified	0
Anomaly Detection through Unsupervised Federated Learning	Sep 9, 2022	Anomaly DetectionFederated Learning	CodeCode Available	0
Suspicious and Anomaly Detection	Sep 8, 2022	Anomaly Detection	—Unverified	0
ReX: A Framework for Incorporating Temporal Information in Model-Agnostic Local Explanation Techniques	Sep 8, 2022	Anomaly DetectionSentiment Analysis	—Unverified	0
W-Transformers : A Wavelet-based Transformer Framework for Univariate Time Series Forecasting	Sep 8, 2022	Anomaly DetectionRecommendation Systems	CodeCode Available	1
Context Recovery and Knowledge Retrieval: A Novel Two-Stream Framework for Video Anomaly Detection	Sep 7, 2022	Anomaly DetectionRetrieval	CodeCode Available	0
Morphology-preserving Autoregressive 3D Generative Modelling of the Brain	Sep 7, 2022	AnatomyAnomaly Detection	CodeCode Available	1

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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