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 4551–4575 of 4856 papers

TitleStatusHype
Anomaly Detection and Localization in Crowded Scenes by Motion-field Shape Description and Similarity-based Statistical Learning—0
AVID: Adversarial Visual Irregularity DetectionCode0
Deep Active Learning for Anomaly Detection—0
Early Cancer Detection in Blood Vessels Using Mobile Nanosensors—0
Localized Multiple Kernel Learning for Anomaly Detection: One-class ClassificationCode0
A Compact Convolutional Neural Network for Textured Surface Anomaly DetectionCode0
Extending Dynamic Bayesian Networks for Anomaly Detection in Complex Logs—0
GANomaly: Semi-Supervised Anomaly Detection via Adversarial TrainingCode0
Sequence Aggregation Rules for Anomaly Detection in Computer Network Traffic—0
N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep AutoencodersCode0
Incorporating Privileged Information to Unsupervised Anomaly Detection—0
Population Anomaly Detection through Deep Gaussianization—0
Anomaly and Change Detection in Graph Streams through Constant-Curvature Manifold Embeddings—0
Detection of Unknown Anomalies in Streaming Videos with Generative Energy-based Boltzmann Models—0
Dysarthric speech evaluation: automatic and perceptual approaches—0
Generating Synthetic X-ray Images of a Person from the Surface Geometry—0
EMO\&LY (EMOtion and AnomaLY) : A new corpus for anomaly detection in an audiovisual stream with emotional context.—0
Towards Experienced Anomaly Detector through Reinforcement Learning—0
STAN: Spatio-Temporal Adversarial Networks for Abnormal Event Detection—0
Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly DetectionCode0
Deep Learning on Operational Facility Data Related to Large-Scale Distributed Area Scientific Workflows—0
Group Anomaly Detection using Deep Generative ModelsCode0
Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier featuresCode0
Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR ImagesCode0
E-commerce Anomaly Detection: A Bayesian Semi-Supervised Tensor Decomposition Approach using Natural Gradients—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