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 4121–4130 of 4856 papers

Title	Date	Tasks	Status
Large Language Models for Anomaly Detection in Computational Workflows: from Supervised Fine-Tuning to In-Context Learning	Jul 24, 2024	Anomaly DetectionIn-Context Learning	CodeCode Available
Looking at Model Debiasing through the Lens of Anomaly Detection	Jul 24, 2024	Anomaly Detection	CodeCode Available
A Compact Convolutional Neural Network for Textured Surface Anomaly Detection	May 18, 2018	Anomaly DetectionAnomaly Segmentation	CodeCode Available
Language-Assisted Feature Transformation for Anomaly Detection	Mar 3, 2025	Anomaly Detection	CodeCode Available
Landmine Detection Using Autoencoders on Multi-polarization GPR Volumetric Data	Oct 2, 2018	Anomaly DetectionGPR	CodeCode Available
Real-time Safety Assessment of Dynamic Systems in Non-stationary Environments: A Review of Methods and Techniques	Apr 25, 2023	Active LearningAnomaly Detection	CodeCode Available
Known Unknowns: Uncertainty Quality in Bayesian Neural Networks	Dec 5, 2016	Anomaly DetectionKnown Unknowns	CodeCode Available
Using Visual Anomaly Detection for Task Execution Monitoring	Jul 29, 2021	Anomaly DetectionOptical Flow Estimation	CodeCode Available
Divide and Conquer in Video Anomaly Detection: A Comprehensive Review and New Approach	Sep 26, 2023	Anomaly DetectionPhilosophy	CodeCode Available
Low-Latency Video Anonymization for Crowd Anomaly Detection: Privacy vs. Performance	Oct 24, 2024	Anomaly DetectionDe-identification	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	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
7	DDAD	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	PBAS	Detection AUROC	99.8	—	Unverified
10	HETMM	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	DDAD	Detection AUROC	98.9	—	Unverified
5	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	INP-Former ViT-B (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