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

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Showing 2401–2410 of 4856 papers

Title	Date	Tasks	Status
Rule-Based Error Detection and Correction to Operationalize Movement Trajectory Classification	Aug 28, 2023	Anomaly Detection	CodeCode Available
Neural Network Training Strategy to Enhance Anomaly Detection Performance: A Perspective on Reconstruction Loss Amplification	Aug 28, 2023	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available
Tackling Diverse Minorities in Imbalanced Classification	Aug 28, 2023	Anomaly DetectionClassification	—Unverified
A Bayesian Non-parametric Approach to Generative Models: Integrating Variational Autoencoder and Generative Adversarial Networks using Wasserstein and Maximum Mean Discrepancy	Aug 27, 2023	Anomaly DetectionData Augmentation	—Unverified
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
Bias in Unsupervised Anomaly Detection in Brain MRI	Aug 26, 2023	Alzheimer's Disease DetectionAnomaly Detection	—Unverified
A Generic Machine Learning Framework for Fully-Unsupervised Anomaly Detection with Contaminated Data	Aug 25, 2023	Anomaly DetectionTime Series	—Unverified
Representing Timed Automata and Timing Anomalies of Cyber-Physical Production Systems in Knowledge Graphs	Aug 25, 2023	Anomaly DetectionKnowledge Graphs	—Unverified
Burnt area extraction from high-resolution satellite images based on anomaly detection	Aug 25, 2023	Anomaly Detection	—Unverified
Multivariate Time Series Anomaly Detection: Fancy Algorithms and Flawed Evaluation Methodology	Aug 24, 2023	Anomaly DetectionTime Series	CodeCode Available

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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