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 2171–2180 of 4856 papers

Title	Date	Tasks	Status	Hype
TSI-GAN: Unsupervised Time Series Anomaly Detection using Convolutional Cycle-Consistent Generative Adversarial Networks	Mar 22, 2023	Anomaly DetectionAutonomous Driving	CodeCode Available	1
Anomaly Detection in Aeronautics Data with Quantum-compatible Discrete Deep Generative Model	Mar 22, 2023	Anomaly DetectionTime Series	—Unverified	0
A Novel Multi-Stage Approach for Hierarchical Intrusion Detection	Mar 21, 2023	Anomaly DetectionIntrusion Detection	CodeCode Available	0
Unlocking Layer-wise Relevance Propagation for Autoencoders	Mar 21, 2023	Anomaly DetectionMachine Translation	—Unverified	0
Dens-PU: PU Learning with Density-Based Positive Labeled Augmentation	Mar 21, 2023	Anomaly DetectionBinary Classification	—Unverified	0
Defect Detection Approaches Based on Simulated Reference Image	Mar 21, 2023	Anomaly DetectionDefect Detection	—Unverified	0
Focus or Not: A Baseline for Anomaly Event Detection On the Open Public Places with Satellite Images	Mar 21, 2023	Anomaly DetectionChange Detection	CodeCode Available	0
Did You Train on My Dataset? Towards Public Dataset Protection with Clean-Label Backdoor Watermarking	Mar 20, 2023	Anomaly Detection	CodeCode Available	0
PseudoBound: Limiting the anomaly reconstruction capability of one-class classifiers using pseudo anomalies	Mar 19, 2023	Anomaly DetectionOne-Class Classification	—Unverified	0
GADformer: A Transparent Transformer Model for Group Anomaly Detection on Trajectories	Mar 17, 2023	Anomaly DetectionGroup Anomaly Detection	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	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