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 3161–3170 of 4856 papers

Title	Date	Tasks	Status
IGRF-RFE: A Hybrid Feature Selection Method for MLP-based Network Intrusion Detection on UNSW-NB15 Dataset	Mar 30, 2022	Anomaly Detectionfeature selection	—Unverified
syslrn: Learning What to Monitor for Efficient Anomaly Detection	Mar 29, 2022	Anomaly Detection	CodeCode Available
Contextual Information Based Anomaly Detection for a Multi-Scene UAV Aerial Videos	Mar 29, 2022	Anomaly DetectionManagement	CodeCode Available
Radial Autoencoders for Enhanced Anomaly Detection	Mar 29, 2022	Anomaly DetectionClassification	—Unverified
PAEDID: Patch Autoencoder Based Deep Image Decomposition For Pixel-level Defective Region Segmentation	Mar 28, 2022	Anomaly Detection	—Unverified
Semi-supervised anomaly detection algorithm based on KL divergence (SAD-KL)	Mar 28, 2022	Anomaly DetectionSemi-supervised Anomaly Detection	—Unverified
A multi-stream deep neural network with late fuzzy fusion for real-world anomaly detection	Mar 27, 2022	Abnormal Event Detection In VideoAnomaly Detection	—Unverified
Stabilizing Adversarially Learned One-Class Novelty Detection Using Pseudo Anomalies	Mar 25, 2022	Anomaly DetectionMedical Diagnosis	—Unverified
Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos	Mar 25, 2022	Anomaly DetectionClustering	—Unverified
From MIM-Based GAN to Anomaly Detection:Event Probability Influence on Generative Adversarial Networks	Mar 25, 2022	Anomaly Detection	—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	GLASS	Detection AUROC	99.9	—	Unverified
5	UniNet	Detection AUROC	99.9	—	Unverified
6	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
7	DDAD	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	PBAS	Detection AUROC	99.8	—	Unverified
10	HETMM	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	DDAD	Detection AUROC	98.9	—	Unverified
5	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	INP-Former ViT-B (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