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 2471–2480 of 4856 papers

Title	Date	Tasks	Status
A Recover-then-Discriminate Framework for Robust Anomaly Detection	Jun 7, 2024	Anomaly Detection	—Unverified
Are Large Language Models Useful for Time Series Data Analysis?	Dec 16, 2024	Anomaly DetectionDecision Making	—Unverified
A Reliable Framework for Human-in-the-Loop Anomaly Detection in Time Series	May 6, 2024	Anomaly DetectionTime Series	—Unverified
A Representation Learning Approach to Feature Drift Detection in Wireless Networks	May 15, 2025	Anomaly DetectionDrift Detection	—Unverified
Are pre-trained CNNs good feature extractors for anomaly detection in surveillance videos?	Nov 20, 2018	Anomaly DetectionAnomaly Detection In Surveillance Videos	—Unverified
A Review of Computer Vision Methods in Network Security	May 7, 2020	Anomaly DetectionMalware Detection	—Unverified
A Review of Machine Learning based Anomaly Detection Techniques	Jul 27, 2013	Anomaly DetectionBIG-bench Machine Learning	—Unverified
A review of neural network algorithms and their applications in supercritical extraction	Oct 31, 2020	Anomaly DetectionChemical Process	—Unverified
A Review of Open Source Software Tools for Time Series Analysis	Mar 10, 2022	Anomaly DetectionTime Series	—Unverified
A Review of Physics-Informed Machine Learning Methods with Applications to Condition Monitoring and Anomaly Detection	Jan 22, 2024	Anomaly DetectionComputational Efficiency	—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	UniNet	Detection AUROC	99.9	—	Unverified
5	GLASS	Detection AUROC	99.9	—	Unverified
6	PBAS	Detection AUROC	99.8	—	Unverified
7	HETMM	Detection AUROC	99.8	—	Unverified
8	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	EfficientAD (early stopping)	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	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	DDAD	Detection AUROC	98.9	—	Unverified
7	GLASS	Detection AUROC	98.8	—	Unverified
8	DiffusionAD	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