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

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Showing 181–190 of 4856 papers

Title	Date	Tasks	Status
Secure Cluster-Based Hierarchical Federated Learning in Vehicular Networks	May 2, 2025	Anomaly DetectionFederated Learning	—Unverified
AI-Driven IRM: Transforming insider risk management with adaptive scoring and LLM-based threat detection	May 1, 2025	Anomaly DetectionFederated Learning	—Unverified
CICADA: Cross-Domain Interpretable Coding for Anomaly Detection and Adaptation in Multivariate Time Series	May 1, 2025	Anomaly DetectionMeta-Learning	—Unverified
LLM-Based Threat Detection and Prevention Framework for IoT Ecosystems	May 1, 2025	Anomaly DetectionLanguage Modeling	—Unverified
Enhanced semi-supervised stamping process monitoring with physically-informed feature extraction	Apr 30, 2025	Anomaly DetectionSemi-supervised Anomaly Detection	—Unverified
Convolutional Autoencoders for Data Compression and Anomaly Detection in Small Satellite Technologies	Apr 29, 2025	Anomaly DetectionData Compression	—Unverified
On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks	Apr 29, 2025	Anomaly DetectionBenchmarking	—Unverified
GiBy: A Giant-Step Baby-Step Classifier For Anomaly Detection In Industrial Control Systems	Apr 29, 2025	Anomaly DetectionDimensionality Reduction	—Unverified
LR-IAD:Mask-Free Industrial Anomaly Detection with Logical Reasoning	Apr 28, 2025	Anomaly DetectionLogical Reasoning	CodeCode Available
AGATE: Stealthy Black-box Watermarking for Multimodal Model Copyright Protection	Apr 28, 2025	Adversarial AttackAnomaly Detection	—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	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