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 1291–1300 of 4856 papers

Title	Date	Tasks	Status
A Review of Machine Learning based Anomaly Detection Techniques	Jul 27, 2013	Anomaly DetectionBIG-bench Machine Learning	—Unverified
A Review of Computer Vision Methods in Network Security	May 7, 2020	Anomaly DetectionMalware Detection	—Unverified
An Explainable Deep Learning Framework for Brain Stroke and Tumor Progression via MRI Interpretation	Jun 10, 2025	Anomaly DetectionData Augmentation	—Unverified
A Contrario multi-scale anomaly detection method for industrial quality inspection	May 3, 2022	Anomaly Detection	—Unverified
An Explainable Artificial Intelligence Approach for Unsupervised Fault Detection and Diagnosis in Rotating Machinery	Feb 23, 2021	Anomaly DetectionBIG-bench Machine Learning	—Unverified
An Explainable Anomaly Detection Framework for Monitoring Depression and Anxiety Using Consumer Wearable Devices	May 5, 2025	Anomaly 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
Advancing Video Anomaly Detection: A Concise Review and a New Dataset	Feb 7, 2024	Anomaly DetectionDiversity	—Unverified
ABBA: Adaptive Brownian bridge-based symbolic aggregation of time series	Mar 27, 2020	Anomaly DetectionClustering	—Unverified
Visual Anomaly Detection under Complex View-Illumination Interplay: A Large-Scale Benchmark	May 16, 2025	Anomaly DetectionBenchmarking	—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