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 4161–4170 of 4856 papers

Title	Date	Tasks	Status
An Empirical Investigation of Contextualized Number Prediction	Oct 20, 2020	Anomaly DetectionPrediction	CodeCode Available
Discriminative Graph-level Anomaly Detection via Dual-students-teacher Model	Aug 3, 2023	Anomaly Detection	CodeCode Available
Learning Global and Local Features of Normal Brain Anatomy for Unsupervised Abnormality Detection	May 26, 2020	AnatomyAnomaly Detection	CodeCode Available
CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP	Dec 5, 2024	Anomaly ClassificationAnomaly Detection	CodeCode Available
A Hybrid Artificial Intelligence System for Automated EEG Background Analysis and Report Generation	Nov 15, 2024	Anomaly DetectionDiagnostic	CodeCode Available
Recurrent Neural Network Language Models for Open Vocabulary Event-Level Cyber Anomaly Detection	Dec 2, 2017	Anomaly DetectionFeature Engineering	CodeCode Available
A predictive model for the identification of learning styles in MOOC environments	Oct 12, 2019	Anomaly DetectionAutomatic Machine Learning Model Selection	CodeCode Available
Sub-Adjacent Transformer: Improving Time Series Anomaly Detection with Reconstruction Error from Sub-Adjacent Neighborhoods	Apr 27, 2024	Anomaly DetectionTime Series	CodeCode Available
AHEAD: A Triple Attention Based Heterogeneous Graph Anomaly Detection Approach	Aug 17, 2022	Anomaly DetectionAttribute	CodeCode Available
Transformer based Models for Unsupervised Anomaly Segmentation in Brain MR Images	Jul 5, 2022	Anomaly DetectionAnomaly Segmentation	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	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