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 651–660 of 4856 papers

Title	Date	Tasks	Status	Hype
ROADS: Robust Prompt-driven Multi-Class Anomaly Detection under Domain Shift	Nov 25, 2024	Anomaly DetectionMulti-class Anomaly Detection	—Unverified	0
FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data	Nov 25, 2024	Anomaly DetectionOne-Class Classification	CodeCode Available	1
Unsupervised Event Outlier Detection in Continuous Time	Nov 25, 2024	Anomaly DetectionData Augmentation	—Unverified	0
Graph Adapter of EEG Foundation Models for Parameter Efficient Fine Tuning	Nov 25, 2024	Anomaly DetectionEEG	—Unverified	0
Circuit design in biology and machine learning. II. Anomaly detection	Nov 23, 2024	Anomaly ClassificationAnomaly Detection	—Unverified	0
TPLogAD: Unsupervised Log Anomaly Detection Based on Event Templates and Key Parameters	Nov 22, 2024	Anomaly DetectionDiversity	—Unverified	0
Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing	Nov 22, 2024	Anomaly Detection	CodeCode Available	0
Information Extraction from Heterogeneous Documents without Ground Truth Labels using Synthetic Label Generation and Knowledge Distillation	Nov 22, 2024	Anomaly Detectiondocument understanding	—Unverified	0
Deep Learning for Cross-Border Transaction Anomaly Detection in Anti-Money Laundering Systems	Nov 21, 2024	Anomaly DetectionContrastive Learning	—Unverified	0
Are Anomaly Scores Telling the Whole Story? A Benchmark for Multilevel Anomaly Detection	Nov 21, 2024	Anomaly Detection	—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