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 4031–4040 of 4856 papers

Title	Date	Tasks	Status	Hype
Deep Nearest Neighbor Anomaly Detection	Feb 24, 2020	Anomaly Detection	—Unverified	0
One-Class Graph Neural Networks for Anomaly Detection in Attributed Networks	Feb 22, 2020	Anomaly DetectionGeneral Classification	CodeCode Available	1
RobustPeriod: Time-Frequency Mining for Robust Multiple Periodicity Detection	Feb 21, 2020	Anomaly DetectionClustering	CodeCode Available	1
RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networks	Feb 21, 2020	Anomaly DetectionData Augmentation	—Unverified	0
A flexible outlier detector based on a topology given by graph communities	Feb 18, 2020	Anomaly Detection	—Unverified	0
Correlation-aware Deep Generative Model for Unsupervised Anomaly Detection	Feb 18, 2020	Anomaly DetectionDecoder	CodeCode Available	1
Taurus: A Data Plane Architecture for Per-Packet ML	Feb 12, 2020	Anomaly DetectionBIG-bench Machine Learning	—Unverified	0
A review on outlier/anomaly detection in time series data	Feb 11, 2020	Anomaly DetectionOutlier Detection	—Unverified	0
Machine Learning-Assisted Anomaly Detection in Maritime Navigation Using AIS Data	Feb 10, 2020	Anomaly DetectionBIG-bench Machine Learning	—Unverified	0
AnomalyDAE: Dual autoencoder for anomaly detection on attributed networks	Feb 10, 2020	Anomaly DetectionAttribute	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	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