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 4801–4825 of 4856 papers

TitleStatusHype
Image anomaly detection and prediction scheme based on SSA optimized ResNet50-BiGRU model—0
Image Anomaly Detection by Aggregating Deep Pyramidal Representations—0
Image-based Deep Learning for Smart Digital Twins: a Review—0
Image-Based Jet Analysis—0
Image-based Plant Disease Diagnosis with Unsupervised Anomaly Detection Based on Reconstructability of Colors—0
Image Captioning and Classification of Dangerous Situations—0
Image-Hashing-Based Anomaly Detection for Privacy-Preserving Online Proctoring—0
Image quality assessment for closed-loop computer-assisted lung ultrasound—0
Image Synthesis as a Pretext for Unsupervised Histopathological Diagnosis—0
Image/Video Deep Anomaly Detection: A Survey—0
Imbalanced Aircraft Data Anomaly Detection—0
Anomaly Detection in Additive Manufacturing Processes using Supervised Classification with Imbalanced Sensor Data based on Generative Adversarial Network—0
iMedImage Technical Report—0
Impact of Deep Learning Libraries on Online Adaptive Lightweight Time Series Anomaly Detection—0
Impact of Inaccurate Contamination Ratio on Robust Unsupervised Anomaly Detection—0
Impact of Recurrent Neural Networks and Deep Learning Frameworks on Real-time Lightweight Time Series Anomaly Detection—0
Implementing Active Learning in Cybersecurity: Detecting Anomalies in Redacted Emails—0
Implementing Immune Repertoire Models Using Weighted Finite State Machines—0
Sobolev Space Regularised Pre Density Models—0
Improved Anomaly Detection in Crowded Scenes via Cell-based Analysis of Foreground Speed, Size and Texture—0
Detection and Imputation based Two-Stage Denoising Diffusion Power System Measurement Recovery under Cyber-Physical Uncertainties—0
Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations—0
Improved YOLOv7x-Based Defect Detection Algorithm for Power Equipment—0
Improving Interpretability of Scores in Anomaly Detection Based on Gaussian-Bernoulli Restricted Boltzmann Machine—0
Improving log-based anomaly detection through learned adaptive filter—0
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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