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 2881–2890 of 4856 papers

Title	Date	Tasks	Status	Hype
Context-Dependent Anomaly Detection with Knowledge Graph Embedding Models	Mar 17, 2022	Anomaly DetectionGraph Embedding	—Unverified	0
PiDAn: A Coherence Optimization Approach for Backdoor Attack Detection and Mitigation in Deep Neural Networks	Mar 17, 2022	Anomaly DetectionBackdoor Attack	—Unverified	0
Driving Anomaly Detection Using Conditional Generative Adversarial Network	Mar 15, 2022	Anomaly DetectionGenerative Adversarial Network	—Unverified	0
Sensitivity Estimation for Dark Matter Subhalos in Synthetic Gaia DR2 using Deep Learning	Mar 15, 2022	Anomaly DetectionSensitivity	—Unverified	0
A Framework for Verifiable and Auditable Federated Anomaly Detection	Mar 15, 2022	Anomaly DetectionEnsemble Learning	—Unverified	0
Practical data monitoring in the internet-services domain	Mar 15, 2022	Anomaly Detection	CodeCode Available	0
Feature space reduction as data preprocessing for the anomaly detection	Mar 13, 2022	Anomaly Detection	CodeCode Available	0
LesionPaste: One-Shot Anomaly Detection for Medical Images	Mar 12, 2022	Anomaly DetectionMedical Image Analysis	—Unverified	0
DeepTrust: A Reliable Financial Knowledge Retrieval Framework For Explaining Extreme Pricing Anomalies	Mar 11, 2022	Anomaly DetectionInformation Retrieval	CodeCode Available	1
Multiple Inputs Neural Networks for Medicare fraud Detection	Mar 11, 2022	Anomaly DetectionFraud Detection	—Unverified	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