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 1791–1800 of 4856 papers

Title	Date	Tasks	Status
Unsupervised Anomaly Detection Using Diffusion Trend Analysis	Jul 12, 2024	Anomaly DetectionDenoising	—Unverified
BoBa: Boosting Backdoor Detection through Data Distribution Inference in Federated Learning	Jul 12, 2024	Anomaly DetectionBackdoor Attack	—Unverified
Deep Learning for Network Anomaly Detection under Data Contamination: Evaluating Robustness and Mitigating Performance Degradation	Jul 11, 2024	Anomaly Detection	—Unverified
Real-Time Anomaly Detection and Reactive Planning with Large Language Models	Jul 11, 2024	Anomaly DetectionAutonomous Vehicles	—Unverified
Integrating Ontology Design with the CRISP-DM in the context of Cyber-Physical Systems Maintenance	Jul 9, 2024	Anomaly Detection	—Unverified
Ensembled Cold-Diffusion Restorations for Unsupervised Anomaly Detection	Jul 9, 2024	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available
TeVAE: A Variational Autoencoder Approach for Discrete Online Anomaly Detection in Variable-state Multivariate Time-series Data	Jul 9, 2024	Anomaly DetectionTime Series	CodeCode Available
F2PAD: A General Optimization Framework for Feature-Level to Pixel-Level Anomaly Detection	Jul 9, 2024	Anomaly DetectionAnomaly Localization	—Unverified
PSPU: Enhanced Positive and Unlabeled Learning by Leveraging Pseudo Supervision	Jul 9, 2024	Anomaly DetectionBinary Classification	—Unverified
Explainable Differential Privacy-Hyperdimensional Computing for Balancing Privacy and Transparency in Additive Manufacturing Monitoring	Jul 9, 2024	Anomaly DetectionDefect Detection	CodeCode Available

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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