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 3451–3460 of 4856 papers

Title	Date	Tasks	Status	Hype
Smart Vectorizations for Single and Multiparameter Persistence	Apr 10, 2021	Anomaly DetectionGraph Classification	CodeCode Available	0
MLF-SC: Incorporating multi-layer features to sparse coding for anomaly detection	Apr 9, 2021	Anomaly Detection	CodeCode Available	1
Brain Surface Reconstruction from MRI Images Based on Segmentation Networks Applying Signed Distance Maps	Apr 9, 2021	Anomaly DetectionSkull Stripping	—Unverified	0
Zero-bias Deep Learning Enabled Quick and Reliable Abnormality Detection in IoT	Apr 8, 2021	Anomaly DetectionChange Point Detection	CodeCode Available	0
Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoT	Apr 8, 2021	Anomaly DetectionTime Series	CodeCode Available	1
CutPaste: Self-Supervised Learning for Anomaly Detection and Localization	Apr 8, 2021	Anomaly ClassificationAnomaly Detection	CodeCode Available	1
Concentration bounds for the empirical angular measure with statistical learning applications	Apr 7, 2021	Anomaly DetectionBinary Classification	CodeCode Available	0
Concentration Inequalities for Two-Sample Rank Processes with Application to Bipartite Ranking	Apr 7, 2021	Anomaly DetectionInformation Retrieval	CodeCode Available	0
Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection	Apr 5, 2021	Anomaly DetectionSensitivity	—Unverified	0
Semi-supervised Variational Temporal Convolutional Network for IoT Communication Multi-anomaly Detection	Apr 5, 2021	Anomaly Detection	—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