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 871–880 of 4856 papers

Title	Date	Tasks	Status
OML-AD: Online Machine Learning for Anomaly Detection in Time Series Data	Sep 15, 2024	Anomaly DetectionComputational Efficiency	CodeCode Available
Matrix Profile for Anomaly Detection on Multidimensional Time Series	Sep 14, 2024	Anomaly DetectionTime Series	—Unverified
Optimal Classification-based Anomaly Detection with Neural Networks: Theory and Practice	Sep 13, 2024	Anomaly DetectionBinary Classification	CodeCode Available
A Survey of Anomaly Detection in In-Vehicle Networks	Sep 11, 2024	Anomaly DetectionSurvey	—Unverified
Ensemble Methods for Sequence Classification with Hidden Markov Models	Sep 11, 2024	Anomaly DetectionBinary Classification	—Unverified
A Continual and Incremental Learning Approach for TinyML On-device Training Using Dataset Distillation and Model Size Adaption	Sep 11, 2024	Anomaly DetectionComputational Efficiency	—Unverified
Atom dimension adaptation for infinite set dictionary learning	Sep 10, 2024	Anomaly DetectionDictionary Learning	—Unverified
Context Enhancement with Reconstruction as Sequence for Unified Unsupervised Anomaly Detection	Sep 10, 2024	Anomaly DetectionMulti-class Anomaly Detection	CodeCode Available
GeMuCo: Generalized Multisensory Correlational Model for Body Schema Learning	Sep 10, 2024	Anomaly DetectionState Estimation	—Unverified
Texture-AD: An Anomaly Detection Dataset and Benchmark for Real Algorithm Development	Sep 10, 2024	Anomaly DetectionDefect Detection	—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