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 3291–3300 of 4856 papers

Title	Date	Tasks	Status
Timeseria: an object-oriented time series processing library	Oct 12, 2024	Anomaly DetectionTime Series	—Unverified
Time-series Anomaly Detection based on Difference Subspace between Signal Subspaces	Mar 31, 2023	Anomaly DetectionTime Series	—Unverified
Time Series Anomaly Detection; Detection of anomalous drops with limited features and sparse examples in noisy highly periodic data	Aug 11, 2017	Anomaly ClassificationAnomaly Detection	—Unverified
Time Series Anomaly Detection for Smart Grids: A Survey	Jul 16, 2021	Anomaly DetectionSurvey	—Unverified
Time Series Anomaly Detection in Smart Homes: A Deep Learning Approach	Feb 28, 2023	Anomaly DetectionScheduling	—Unverified
Time Series Anomaly Detection Using Convolutional Neural Networks and Transfer Learning	May 31, 2019	Anomaly DetectionTime Series	—Unverified
Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network	Dec 1, 2020	Anomaly DetectionClustering	—Unverified
Harnessing Contrastive Learning and Neural Transformation for Time Series Anomaly Detection	Apr 16, 2023	Anomaly DetectionContrastive Learning	—Unverified
Time Series Anomaly Detection with CNN for Environmental Sensors in Healthcare-IoT	Jul 30, 2024	Anomaly DetectionTime Series	—Unverified
Time Series Anomaly Detection with label-free Model Selection	Jun 11, 2021	Anomaly DetectionEnsemble 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	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