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 1831–1840 of 4856 papers

Title	Date	Tasks	Status	Hype
Exploring Human Crowd Patterns and Categorization in Video Footage for Enhanced Security and Surveillance using Computer Vision and Machine Learning	Aug 26, 2023	Anomaly DetectionOptical Flow Estimation	—Unverified	0
A Generic Machine Learning Framework for Fully-Unsupervised Anomaly Detection with Contaminated Data	Aug 25, 2023	Anomaly DetectionTime Series	—Unverified	0
Burnt area extraction from high-resolution satellite images based on anomaly detection	Aug 25, 2023	Anomaly Detection	—Unverified	0
Representing Timed Automata and Timing Anomalies of Cyber-Physical Production Systems in Knowledge Graphs	Aug 25, 2023	Anomaly DetectionKnowledge Graphs	—Unverified	0
Try with Simpler -- An Evaluation of Improved Principal Component Analysis in Log-based Anomaly Detection	Aug 24, 2023	Anomaly Detection	—Unverified	0
Multivariate Time Series Anomaly Detection: Fancy Algorithms and Flawed Evaluation Methodology	Aug 24, 2023	Anomaly DetectionTime Series	CodeCode Available	0
Contaminated Multivariate Time-Series Anomaly Detection with Spatio-Temporal Graph Conditional Diffusion Models	Aug 24, 2023	Anomaly DetectionTime Series	—Unverified	0
REB: Reducing Biases in Representation for Industrial Anomaly Detection	Aug 24, 2023	Anomaly DetectionDefect Detection	CodeCode Available	1
Low-count Time Series Anomaly Detection	Aug 24, 2023	Anomaly DetectionTime Series	—Unverified	0
Exploring the Optimization Objective of One-Class Classification for Anomaly Detection	Aug 23, 2023	Anomaly DetectionOne-Class Classification	—Unverified	0

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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