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 1391–1400 of 4856 papers

Title	Date	Tasks	Status	Hype
Deep Learning-Driven Anomaly Detection for Green IoT Edge Networks	Mar 1, 2024	Anomaly DetectionDeep Learning	—Unverified	0
Dimensionality reduction techniques to support insider trading detection	Mar 1, 2024	Anomaly DetectionContextual Anomaly Detection	—Unverified	0
The Impact of Frequency Bands on Acoustic Anomaly Detection of Machines using Deep Learning Based Model	Mar 1, 2024	Anomaly DetectionData Augmentation	—Unverified	0
UniTS: A Unified Multi-Task Time Series Model	Feb 29, 2024	Anomaly DetectionImputation	CodeCode Available	4
A SAM-guided Two-stream Lightweight Model for Anomaly Detection	Feb 29, 2024	Anomaly DetectionDecoder	CodeCode Available	1
A Novel Approach to Industrial Defect Generation through Blended Latent Diffusion Model with Online Adaptation	Feb 29, 2024	Anomaly DetectionDecoder	CodeCode Available	2
COFT-AD: COntrastive Fine-Tuning for Few-Shot Anomaly Detection	Feb 29, 2024	Anomaly Detection	—Unverified	0
Anomaly Detection in Offshore Wind Turbine Structures using Hierarchical Bayesian Modelling	Feb 29, 2024	Anomaly DetectionStructural Health Monitoring	—Unverified	0
Continuous Memory Representation for Anomaly Detection	Feb 28, 2024	Anomaly Detection	CodeCode Available	1
Objective and Interpretable Breast Cosmesis Evaluation with Attention Guided Denoising Diffusion Anomaly Detection Model	Feb 28, 2024	Anomaly DetectionDenoising	—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	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