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 2861–2870 of 4856 papers

Title	Date	Tasks	Status	Hype
Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos	Mar 25, 2022	Anomaly DetectionClustering	—Unverified	0
Stabilizing Adversarially Learned One-Class Novelty Detection Using Pseudo Anomalies	Mar 25, 2022	Anomaly DetectionMedical Diagnosis	—Unverified	0
From MIM-Based GAN to Anomaly Detection:Event Probability Influence on Generative Adversarial Networks	Mar 25, 2022	Anomaly Detection	—Unverified	0
Bayesian Nonparametric Submodular Video Partition for Robust Anomaly Detection	Mar 24, 2022	Anomaly DetectionMultiple Instance Learning	CodeCode Available	0
SIFT and SURF based feature extraction for the anomaly detection	Mar 24, 2022	Anomaly DetectionOne-class classifier	CodeCode Available	0
Domain-Generalized Textured Surface Anomaly Detection	Mar 23, 2022	Anomaly DetectionDomain Generalization	—Unverified	0
Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection	Mar 23, 2022	Anomaly DetectionMultiple Instance Learning	CodeCode Available	1
Unsupervised Anomaly Detection in Medical Images with a Memory-augmented Multi-level Cross-attentional Masked Autoencoder	Mar 22, 2022	Anomaly DetectionImage Reconstruction	CodeCode Available	0
Two methods for Jamming Identification in UAVs Networks using New Synthetic Dataset	Mar 21, 2022	Anomaly DetectionTime Series	—Unverified	0
FGAN: Federated Generative Adversarial Networks for Anomaly Detection in Network Traffic	Mar 21, 2022	Anomaly DetectionIntrusion 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	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