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 1431–1440 of 4856 papers

Title	Date	Tasks	Status
Audio-based Anomaly Detection in Industrial Machines Using Deep One-Class Support Vector Data Description	Dec 14, 2024	Anomaly DetectionComputational Efficiency	—Unverified
Know Unreported Roadway Incidents in Real-time: A Deep Learning Framework for Early Traffic Anomaly Detection	Dec 14, 2024	Anomaly Detection	—Unverified
FDM-Bench: A Comprehensive Benchmark for Evaluating Large Language Models in Additive Manufacturing Tasks	Dec 13, 2024	Anomaly Detection	CodeCode Available
Multimodal Industrial Anomaly Detection by Crossmodal Reverse Distillation	Dec 12, 2024	Anomaly DetectionKnowledge Distillation	CodeCode Available
Enhancing Cybersecurity in IoT Networks: A Deep Learning Approach to Anomaly Detection	Dec 11, 2024	Anomaly Detection	—Unverified
PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection	Dec 11, 2024	Anomaly DetectionFraud Detection	—Unverified
GradStop: Exploring Training Dynamics in Unsupervised Outlier Detection through Gradient	Dec 11, 2024	Anomaly DetectionOutlier Detection	CodeCode Available
Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection	Dec 11, 2024	Anomaly DetectionComputational Efficiency	—Unverified
Anomaly detection using Diffusion-based methods	Dec 10, 2024	Anomaly DetectionDecoder	—Unverified
PASTA: Neural Architecture Search for Anomaly Detection in Multivariate Time Series	Dec 9, 2024	Anomaly DetectionNeural Architecture Search	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