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 2441–2450 of 4856 papers

Title	Date	Tasks	Status
Appearance Blur-driven AutoEncoder and Motion-guided Memory Module for Video Anomaly Detection	Sep 26, 2024	Anomaly DetectionDomain Adaptation	—Unverified
Application Of ADNN For Background Subtraction In Smart Surveillance System	Dec 31, 2022	Anomaly DetectionMotion Detection	—Unverified
Application of advanced machine learning algorithms for anomaly detection and quantitative prediction in protein A chromatography	Sep 5, 2022	Anomaly DetectionDimensionality Reduction	—Unverified
Application of a Dynamic Line Graph Neural Network for Intrusion Detection With Semisupervised Learning	Dec 2, 2022	Anomaly DetectionGraph Neural Network	—Unverified
Application of MUSIC-type imaging for anomaly detection without background information	Jul 3, 2023	Anomaly Detection	—Unverified
Application of Unsupervised Domain Adaptation for Structural MRI Analysis	Dec 26, 2022	Anomaly DetectionBinary Classification	—Unverified
Applications of Artificial Intelligence, Machine Learning and related techniques for Computer Networking Systems	Apr 21, 2021	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review	Oct 22, 2021	Anomaly DetectionSystematic Literature Review	—Unverified
Applications of Machine Learning to the Identification of Anomalous ER Claims	Jun 16, 2022	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Applications of Recurrent Neural Network for Biometric Authentication & Anomaly Detection	Sep 13, 2021	Anomaly Detectionspeech-recognition	—Unverified

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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