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 4021–4030 of 4856 papers

Title	Date	Tasks	Status	Hype
CRATOS: Cognition of Reliable Algorithm for Time-series Optimal Solution	Mar 3, 2020	Anomaly DetectionClustering	—Unverified	0
Why is the Mahalanobis Distance Effective for Anomaly Detection?	Mar 1, 2020	Anomaly DetectionGeneral Classification	—Unverified	0
Unsupervised Dictionary Learning for Anomaly Detection	Feb 29, 2020	Anomaly DetectionDictionary Learning	—Unverified	0
DROCC: Deep Robust One-Class Classification	Feb 28, 2020	Anomaly DetectionClassification	—Unverified	0
MLography: An Automated Quantitative Metallography Model for Impurities Anomaly Detection using Novel Data Mining and Deep Learning Approach	Feb 27, 2020	Anomaly Detection	CodeCode Available	1
An On-Device Federated Learning Approach for Cooperative Model Update between Edge Devices	Feb 27, 2020	Anomaly DetectionFederated Learning	—Unverified	0
Semi-supervised Anomaly Detection on Attributed Graphs	Feb 27, 2020	Anomaly DetectionAttribute	CodeCode Available	0
Time Series Data Augmentation for Deep Learning: A Survey	Feb 27, 2020	Anomaly DetectionData Augmentation	—Unverified	0
Wavelet-based temporal models of human activity for anomaly detection in smart robot-assisted environments	Feb 26, 2020	Anomaly Detection	—Unverified	0
Unsupervised Discovery, Control, and Disentanglement of Semantic Attributes with Applications to Anomaly Detection	Feb 25, 2020	Anomaly DetectionAttribute	—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	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