SOTAVerified

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

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Showing 1626–1650 of 4856 papers

TitleStatusHype
Semi-Supervised Bone Marrow Lesion Detection from Knee MRI Segmentation Using Mask Inpainting Models—0
MIMII-Gen: Generative Modeling Approach for Simulated Evaluation of Anomalous Sound Detection System—0
Kinematic Detection of Anomalies in Human Trajectory DataCode0
Neural Collaborative Filtering to Detect Anomalies in Human Semantic TrajectoriesCode0
Machine Learning-based vs Deep Learning-based Anomaly Detection in Multivariate Time Series for Spacecraft Attitude Sensors—0
Appearance Blur-driven AutoEncoder and Motion-guided Memory Module for Video Anomaly Detection—0
VL4AD: Vision-Language Models Improve Pixel-wise Anomaly Detection—0
Grading and Anomaly Detection for Automated Retinal Image Analysis using Deep Learning—0
XAI-guided Insulator Anomaly Detection for Imbalanced Datasets—0
Leveraging Unsupervised Learning for Cost-Effective Visual Anomaly Detection—0
Exploring the Impact of Outlier Variability on Anomaly Detection Evaluation Metrics—0
A Multi-Level Approach for Class Imbalance Problem in Federated Learning for Remote Industry 4.0 Applications—0
Anomaly Detection from a Tensor Train Perspective—0
MotifDisco: Motif Causal Discovery For Time Series Motifs—0
Research on Dynamic Data Flow Anomaly Detection based on Machine Learning—0
VARADE: a Variational-based AutoRegressive model for Anomaly Detection on the EdgeCode0
LatentQGAN: A Hybrid QGAN with Classical Convolutional Autoencoder—0
Vision-Language Models Assisted Unsupervised Video Anomaly Detection—0
Combining Switching Mechanism with Re-Initialization and Anomaly Detection for Resiliency of Cyber-Physical Systems—0
Towards the Discovery of Down Syndrome Brain Biomarkers Using Generative Models—0
MeLIAD: Interpretable Few-Shot Anomaly Detection with Metric Learning and Entropy-based Scoring—0
Investigation on domain adaptation of additive manufacturing monitoring systems to enhance digital twin reusability—0
Trustworthy Intrusion Detection: Confidence Estimation Using Latent Space—0
Towards Unbiased Evaluation of Time-series Anomaly DetectorCode0
Cloudy with a Chance of Anomalies: Dynamic Graph Neural Network for Early Detection of Cloud Services' User AnomaliesCode0
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All datasetsMVTec ADVisAMVTec LOCO ADOne-class CIFAR-10CUHK AvenueShanghaiTechFishyscapes L&FUCR Anomaly ArchiveMPDDBTADOne-class CIFAR-100UBnormal

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016—Unverified
2CPR-fast(TensorRT)FPS362—Unverified
3CPR(TensorRT)FPS130—Unverified
4GLASSDetection AUROC99.9—Unverified
5UniNetDetection AUROC99.9—Unverified
6HETMMDetection AUROC99.8—Unverified
7INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9DDADDetection AUROC99.8—Unverified
10PBASDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9—Unverified
5DDADDetection AUROC98.9—Unverified
6Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
7DiffusionADDetection AUROC98.8—Unverified
8GLASSDetection AUROC98.8—Unverified
9TransFusionDetection AUROC98.7—Unverified
10HETMMDetection AUROC98.1—Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3—Unverified
2PSADAvg. Detection AUROC94.9—Unverified