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 2221–2230 of 4856 papers

Title	Date	Tasks	Status	Hype
Unsupervised Pathology Detection: A Deep Dive Into the State of the Art	Mar 1, 2023	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	1
RePAD2: Real-Time, Lightweight, and Adaptive Anomaly Detection for Open-Ended Time Series	Mar 1, 2023	Anomaly DetectionTime Series	—Unverified	0
First-shot anomaly sound detection for machine condition monitoring: A domain generalization baseline	Mar 1, 2023	Anomaly DetectionDomain Generalization	CodeCode Available	1
Texture-Based Input Feature Selection for Action Recognition	Feb 28, 2023	Action RecognitionAnomaly Detection	—Unverified	0
Dissolving Is Amplifying: Towards Fine-Grained Anomaly Detection	Feb 28, 2023	Anomaly DetectionContrastive Learning	CodeCode Available	1
Time Series Anomaly Detection in Smart Homes: A Deep Learning Approach	Feb 28, 2023	Anomaly DetectionScheduling	—Unverified	0
Unsupervised Video Anomaly Detection for Stereotypical Behaviours in Autism	Feb 27, 2023	Activity RecognitionAnomaly Detection	—Unverified	0
Brain subtle anomaly detection based on auto-encoders latent space analysis : application to de novo parkinson patients	Feb 27, 2023	Anomaly DetectionLesion Detection	—Unverified	0
MDF-Net for abnormality detection by fusing X-rays with clinical data	Feb 26, 2023	Anomaly Detection	CodeCode Available	1
Interruptions detection in video conferences	Feb 25, 2023	Anomaly Detection	CodeCode Available	0

Show:10 25 50

← PrevPage 223 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	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