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 3801–3810 of 4856 papers

Title	Date	Tasks	Status
Self-Supervised Transformers for Activity Classification using Ambient Sensors	Nov 22, 2020	Activity RecognitionAnomaly Detection	—Unverified
Efficient Consensus Model based on Proximal Gradient Method applied to Convolutional Sparse Problems	Nov 19, 2020	Anomaly DetectionDictionary Learning	—Unverified
Preparing Weather Data for Real-Time Building Energy Simulation	Nov 19, 2020	Anomaly DetectionImputation	—Unverified
Sub-clusters of Normal Data for Anomaly Detection	Nov 17, 2020	Anomaly DetectionClustering	—Unverified
Learning normal appearance for fetal anomaly screening: Application to the unsupervised detection of Hypoplastic Left Heart Syndrome	Nov 15, 2020	AnatomyAnomaly Detection	—Unverified
Local Anomaly Detection in Videos using Object-Centric Adversarial Learning	Nov 13, 2020	Anomaly DetectionObject	—Unverified
Dependency-based Anomaly Detection: a General Framework and Comprehensive Evaluation	Nov 13, 2020	Anomaly Detectionfeature selection	—Unverified
Image Anomaly Detection by Aggregating Deep Pyramidal Representations	Nov 12, 2020	Anomaly Detection	—Unverified
A Transfer Learning Framework for Anomaly Detection Using Model of Normality	Nov 12, 2020	Anomaly DetectionTransfer Learning	—Unverified
Real-World Anomaly Detection by using Digital Twin Systems and Weakly-Supervised Learning	Nov 12, 2020	Anomaly DetectionClustering	—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	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