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 2081–2090 of 4856 papers

Title	Date	Tasks	Status
G^2uardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clustering	Jun 8, 2023	Anomaly DetectionClustering	—Unverified
Correlated discrete data generation using adversarial training	Apr 3, 2018	Anomaly DetectionImage Generation	—Unverified
Anomaly Detection With Conditional Variational Autoencoders	Oct 12, 2020	Anomaly Detection	—Unverified
Correlated Attention in Transformers for Multivariate Time Series	Nov 20, 2023	Anomaly DetectionImputation	—Unverified
Correlated Anomaly Detection from Large Streaming Data	Dec 19, 2018	Anomaly DetectionEvent Detection	—Unverified
Anomaly detection with a variational autoencoder for Arabic mispronunciation detection	Jun 25, 2024	Anomaly Detection	—Unverified
A Multi-perspective Approach To Anomaly Detection For Self-aware Embodied Agents	Mar 17, 2018	Anomaly Detection	—Unverified
Corn Yield Prediction based on Remotely Sensed Variables Using Variational Autoencoder and Multiple Instance Regression	Nov 23, 2022	Anomaly DetectionCrop Yield Prediction	—Unverified
Anomaly Detection with Adversarially Learned Perturbations of Latent Space	Jul 3, 2022	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
Coresets for Data Discretization and Sine Wave Fitting	Mar 6, 2022	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	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