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 3401–3410 of 4856 papers

Title	Date	Tasks	Status	Hype
Discriminative-Generative Dual Memory Video Anomaly Detection	Apr 29, 2021	Anomaly DetectionTriplet	—Unverified	0
A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly Detection	Apr 29, 2021	Anomaly DetectionDefect Detection	CodeCode Available	1
Image Synthesis as a Pretext for Unsupervised Histopathological Diagnosis	Apr 28, 2021	Anomaly DetectionFace Generation	—Unverified	0
PANDA : Perceptually Aware Neural Detection of Anomalies	Apr 28, 2021	Anomaly DetectionDefect Detection	—Unverified	0
Inpainting Transformer for Anomaly Detection	Apr 28, 2021	Anomaly Detection	CodeCode Available	1
Multi-view Deep One-class Classification: A Systematic Exploration	Apr 27, 2021	Anomaly DetectionClassification	CodeCode Available	0
Extending Isolation Forest for Anomaly Detection in Big Data via K-Means	Apr 27, 2021	Anomaly DetectionIntrusion Detection	—Unverified	0
Incident Detection on Junctions Using Image Processing	Apr 27, 2021	Anomaly DetectionManagement	—Unverified	0
Towards On-Device Federated Learning: A Direct Acyclic Graph-based Blockchain Approach	Apr 27, 2021	Anomaly DetectionFederated Learning	—Unverified	0
ODDObjects: A Framework for Multiclass Unsupervised Anomaly Detection on Masked Objects	Apr 26, 2021	Anomaly DetectionImage Reconstruction	CodeCode Available	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