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 3811–3820 of 4856 papers

Title	Date	Tasks	Status
Multi-scale Anomaly Detection for Big Time Series of Industrial Sensors	Apr 18, 2022	Anomaly DetectionTime Series	—Unverified
Multi-Scale Convolutional LSTM with Transfer Learning for Anomaly Detection in Cellular Networks	Sep 30, 2024	Anomaly DetectionTransfer Learning	—Unverified
Multiscale Fusion for Abnormality Detection and Localization of Distributed Parameter Systems	Oct 10, 2023	Anomaly DetectionFault Detection	—Unverified
Multi-scale Masked Autoencoder for Electrocardiogram Anomaly Detection	Feb 8, 2025	Anomaly Detection	—Unverified
Multi-Scale Memory Comparison for Zero-/Few-Shot Anomaly Detection	Aug 9, 2023	Anomaly DetectionDefect Detection	—Unverified
Multi-Scale Patch-Based Representation Learning for Image Anomaly Detection and Segmentation	Oct 23, 2022	Anomaly DetectionRepresentation Learning	—Unverified
Multi-scale Spatial-temporal Interaction Network for Video Anomaly Detection	Jun 17, 2023	Anomaly DetectionVideo Anomaly Detection	—Unverified
Multi-Scale Video Anomaly Detection by Multi-Grained Spatio-Temporal Representation Learning	Jan 1, 2024	Anomaly DetectionContrastive Learning	—Unverified
Multi-Source Anomaly Detection in Distributed IT Systems	Jan 13, 2021	Anomaly Detection	—Unverified
Multi-Stage Fault Warning for Large Electric Grids Using Anomaly Detection and Machine Learning	Mar 15, 2019	Anomaly DetectionBIG-bench Machine Learning	—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	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