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 3601–3610 of 4856 papers

Title	Date	Tasks	Status
Image Synthesis as a Pretext for Unsupervised Histopathological Diagnosis	Apr 28, 2021	Anomaly DetectionFace Generation	—Unverified
Multi-view Deep One-class Classification: A Systematic Exploration	Apr 27, 2021	Anomaly DetectionClassification	CodeCode Available
Towards On-Device Federated Learning: A Direct Acyclic Graph-based Blockchain Approach	Apr 27, 2021	Anomaly DetectionFederated Learning	—Unverified
Extending Isolation Forest for Anomaly Detection in Big Data via K-Means	Apr 27, 2021	Anomaly DetectionIntrusion Detection	—Unverified
Incident Detection on Junctions Using Image Processing	Apr 27, 2021	Anomaly DetectionManagement	—Unverified
ODDObjects: A Framework for Multiclass Unsupervised Anomaly Detection on Masked Objects	Apr 26, 2021	Anomaly DetectionImage Reconstruction	CodeCode Available
Unsupervised Learning of Multi-level Structures for Anomaly Detection	Apr 25, 2021	AllAnomaly Detection	—Unverified
An Efficient One-Class SVM for Anomaly Detection in the Internet of Things	Apr 22, 2021	Anomaly DetectionNovelty Detection	—Unverified
Software-Defined Edge Computing: A New Architecture Paradigm to Support IoT Data Analysis	Apr 22, 2021	Anomaly DetectionEdge-computing	—Unverified
Unsupervised anomaly detection for a Smart Autonomous Robotic Assistant Surgeon (SARAS)using a deep residual autoencoder	Apr 22, 2021	Anomaly DetectionUnsupervised 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	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