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 721–730 of 4856 papers

Title	Date	Tasks	Status	Hype
A Comprehensive Survey on Graph Anomaly Detection with Deep Learning	Jun 14, 2021	Anomaly DetectionDeep Learning	CodeCode Available	1
MLPerf Tiny Benchmark	Jun 14, 2021	Anomaly DetectionBIG-bench Machine Learning	CodeCode Available	1
Graph Neural Network-Based Anomaly Detection in Multivariate Time Series	Jun 13, 2021	Anomaly DetectionGraph Neural Network	CodeCode Available	1
XBNet : An Extremely Boosted Neural Network	Jun 9, 2021	Anomaly DetectionBreast Cancer Detection	CodeCode Available	1
DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly Detection	Jun 9, 2021	Anomaly DetectionSupervised Anomaly Detection	CodeCode Available	1
Implicit field learning for unsupervised anomaly detection in medical images	Jun 9, 2021	Anomaly DetectionDecoder	CodeCode Available	1
Sketch-Based Anomaly Detection in Streaming Graphs	Jun 8, 2021	Anomaly DetectionIntrusion Detection	CodeCode Available	1
Mean-Shifted Contrastive Loss for Anomaly Detection	Jun 7, 2021	Anomaly DetectionContrastive Learning	CodeCode Available	1
MemStream: Memory-Based Streaming Anomaly Detection	Jun 7, 2021	Anomaly DetectionDenoising	CodeCode Available	1
ToyADMOS2: Another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions	Jun 4, 2021	Anomaly DetectionFault Diagnosis	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