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 2461–2470 of 4856 papers

Title	Date	Tasks	Status	Hype
Generation of Anonymous Chest Radiographs Using Latent Diffusion Models for Training Thoracic Abnormality Classification Systems	Nov 2, 2022	Anomaly DetectionImage Generation	—Unverified	0
Unsupervised Anomaly Detection of Paranasal Anomalies in the Maxillary Sinus	Nov 1, 2022	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified	0
HFN: Heterogeneous Feature Network for Multivariate Time Series Anomaly Detection	Nov 1, 2022	Anomaly DetectionGraph structure learning	—Unverified	0
Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection	Nov 1, 2022	Anomaly DetectionDomain Adaptation	CodeCode Available	1
Siamese Transition Masked Autoencoders as Uniform Unsupervised Visual Anomaly Detector	Nov 1, 2022	Anomaly DetectionDecoder	—Unverified	0
MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model	Nov 1, 2022	Anomaly DetectionBrain Tumor Segmentation	CodeCode Available	3
Tree Detection and Diameter Estimation Based on Deep Learning	Oct 31, 2022	Anomaly DetectionDeep Learning	CodeCode Available	1
Teacher-Student Network for 3D Point Cloud Anomaly Detection with Few Normal Samples	Oct 31, 2022	3D Anomaly DetectionAnomaly Detection	—Unverified	0
SoK: Modeling Explainability in Security Analytics for Interpretability, Trustworthiness, and Usability	Oct 31, 2022	Anomaly DetectionDecision Making	—Unverified	0
Classical ensemble of Quantum-classical ML algorithms for Phishing detection in Ethereum transaction networks	Oct 30, 2022	Anomaly DetectionBenchmarking	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	GLASS	Detection AUROC	99.9	—	Unverified
5	UniNet	Detection AUROC	99.9	—	Unverified
6	HETMM	Detection AUROC	99.8	—	Unverified
7	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	PBAS	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	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	DDAD	Detection AUROC	98.9	—	Unverified
6	Dinomaly ViT-L (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