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 3071–3080 of 4856 papers

Title	Date	Tasks	Status	Hype
SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection	Nov 26, 2021	AnatomyAnomaly Detection	CodeCode Available	1
A Taxonomy of Anomalies in Log Data	Nov 26, 2021	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified	0
SLA^2P: Self-supervised Anomaly Detection with Adversarial Perturbation	Nov 25, 2021	Anomaly DetectionPseudo Label	CodeCode Available	1
A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection	Nov 25, 2021	Anomaly DetectionBIG-bench Machine Learning	—Unverified	0
Deep Representation Learning with an Information-theoretic Loss	Nov 25, 2021	Anomaly DetectionFew-Shot Learning	—Unverified	0
Consensus Synergizes with Memory: A Simple Approach for Anomaly Segmentation in Urban Scenes	Nov 24, 2021	Anomaly DetectionAnomaly Segmentation	—Unverified	0
Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance	Nov 24, 2021	Anomaly DetectionGraph Neural Network	CodeCode Available	0
Efficient Anomaly Detection Using Self-Supervised Multi-Cue Tasks	Nov 24, 2021	Anomaly DetectionColorization	—Unverified	0
Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes	Nov 24, 2021	Anomaly DetectionAnomaly Segmentation	CodeCode Available	1
Link Analysis meets Ontologies: Are Embeddings the Answer?	Nov 23, 2021	Anomaly Detection	CodeCode Available	0

Show:10 25 50

← PrevPage 308 of 486Next →

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