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 3111–3120 of 4856 papers

Title	Date	Tasks	Status	Hype
Evaluation of an Anomaly Detector for Routers using Parameterizable Malware in an IoT Ecosystem	Oct 29, 2021	Anomaly Detection	—Unverified	0
Boosting Anomaly Detection Using Unsupervised Diverse Test-Time Augmentation	Oct 29, 2021	Anomaly Detection	CodeCode Available	0
PEDENet: Image Anomaly Localization via Patch Embedding and Density Estimation	Oct 29, 2021	Anomaly DetectionAnomaly Localization	—Unverified	0
How to boost autoencoders?	Oct 28, 2021	Anomaly DetectionClustering	—Unverified	0
Multi-Class Anomaly Detection	Oct 28, 2021	Anomaly DetectionMulti-class Anomaly Detection	—Unverified	0
OMASGAN: Out-of-Distribution Minimum Anomaly Score GAN for Sample Generation on the Boundary	Oct 28, 2021	Anomaly DetectionData Augmentation	CodeCode Available	1
Normality-Calibrated Autoencoder for Unsupervised Anomaly Detection on Data Contamination	Oct 28, 2021	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	0
Anomaly-Injected Deep Support Vector Data Description for Text Outlier Detection	Oct 27, 2021	Anomaly DetectionOutlier Detection	—Unverified	0
Sensing Anomalies as Potential Hazards: Datasets and Benchmarks	Oct 27, 2021	Anomaly Detection	CodeCode Available	1
Subtractive Aggregation for Attributed Network Anomaly Detection	Oct 26, 2021	Anomaly DetectionGraph Neural Network	CodeCode Available	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