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 2641–2650 of 4856 papers

Title	Date	Tasks	Status
Time-series Anomaly Detection based on Difference Subspace between Signal Subspaces	Mar 31, 2023	Anomaly DetectionTime Series	—Unverified
Unsupervised crack detection on complex stone masonry surfaces	Mar 31, 2023	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available
ISSTAD: Incremental Self-Supervised Learning Based on Transformer for Anomaly Detection and Localization	Mar 30, 2023	Anomaly DetectionAnomaly Localization	CodeCode Available
Protecting Federated Learning from Extreme Model Poisoning Attacks via Multidimensional Time Series Anomaly Detection	Mar 29, 2023	Anomaly DetectionFederated Learning	—Unverified
Unsupervised Anomaly Detection with Local-Sensitive VQVAE and Global-Sensitive Transformers	Mar 29, 2023	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
Improving Object Detection in Medical Image Analysis through Multiple Expert Annotators: An Empirical Investigation	Mar 29, 2023	Anomaly DetectionMedical Image Analysis	—Unverified
Attention Boosted Autoencoder for Building Energy Anomaly Detection	Mar 28, 2023	Anomaly Detection	—Unverified
Disruption Precursor Onset Time Study Based on Semi-supervised Anomaly Detection	Mar 27, 2023	Anomaly DetectionSemi-supervised Anomaly Detection	—Unverified
Topological Pooling on Graphs	Mar 25, 2023	Anomaly DetectionGraph Classification	CodeCode Available
Interpretable Anomaly Detection via Discrete Optimization	Mar 24, 2023	Anomaly DetectionDecision Making	—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