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 2151–2160 of 4856 papers

Title	Date	Tasks	Status
A Model-Free Kullback-Leibler Divergence Filter for Anomaly Detection in Noisy Data Series	May 5, 2024	Anomaly Detection	—Unverified
Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection	Dec 14, 2018	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
Context-Dependent Anomaly Detection with Knowledge Graph Embedding Models	Mar 17, 2022	Anomaly DetectionGraph Embedding	—Unverified
Anomaly Detection using Generative Models and Sum-Product Networks in Mammography Scans	Oct 12, 2022	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
Context-Dependent Anomaly Detection for Low Altitude Traffic Surveillance	Apr 14, 2021	Anomaly DetectionContextual Anomaly Detection	—Unverified
Context-aware Video Anomaly Detection in Long-Term Datasets	Apr 11, 2024	Anomaly DetectionContrastive Learning	—Unverified
‘Am I the Bad One’? Predicting the Moral Judgement of the Crowd Using Pre–trained Language Models	Jun 1, 2022	Anomaly DetectionNatural Language Inference	—Unverified
Generative Pre-Trained Transformer for Cardiac Abnormality Detection	Oct 7, 2021	Anomaly DetectionClassification	—Unverified
Genetic Neural Architecture Search for automatic assessment of human sperm images	Sep 20, 2019	Anomaly DetectionNeural Architecture Search	—Unverified
Global Confidence Degree Based Graph Neural Network for Financial Fraud Detection	Jul 24, 2024	Anomaly DetectionFraud 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