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 3151–3200 of 4856 papers

TitleStatusHype
Generative Optimization Networks for Memory Efficient Data GenerationCode0
An Efficient Anomaly Detection Approach using Cube Sampling with Streaming Data—0
A Multi-Scale A Contrario method for Unsupervised Image Anomaly Detection—0
Proxy-bridged Image Reconstruction Network for Anomaly Detection in Medical Images—0
Stochastic tensor space feature theory with applications to robust machine learning—0
Probabilistic Robust Autoencoders for Outlier Detection—0
Real-Time Predictive Maintenance using Autoencoder Reconstruction and Anomaly Detection—0
Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and LocalizationCode1
A multi-domain splitting framework for time-varying graph structure—0
ANOMALY DETECTION WITH FRAME-GROUP ATTENTION IN SURVEILLANCE VIDEOS—0
Fast Adaptive Anomaly Detection—0
Iterative Bilinear Temporal-Spectral Fusion for Unsupervised Representation Learning in Time Series—0
You May Need both Good-GAN and Bad-GAN for Anomaly Detection—0
S^3ADNet: Sequential Anomaly Detection with Pessimistic Contrastive Learning—0
Anomaly Detection for Tabular Data with Internal Contrastive Learning—0
STRIC: Stacked Residuals of Interpretable Components for Time Series Anomaly Detection—0
A2B-GAN: Utilizing Unannotated Anomalous Images for Anomaly Detection in Medical Image Analysis—0
Patchwise Sparse Dictionary Learning from pre-trained Neural Network Activation Maps for Anomaly Detection in Images—0
No Shifted Augmentations (NSA): strong baselines for self-supervised Anomaly Detection—0
Sequential Deep Learning Architectures for Anomaly Detection in Virtual Network Function Chains—0
Y-GAN: Learning Dual Data Representations for Efficient Anomaly Detection—0
Anomaly Detection for High-Dimensional Data Using Large Deviations Principle—0
An Automated Data Engineering Pipeline for Anomaly Detection of IoT Sensor Data—0
An Energy Efficient Health Monitoring Approach with Wireless Body Area Networks—0
Visual Anomaly Detection for Images: A Survey—0
Distributed Deep Learning for Persistent Monitoring of agricultural Fields—0
Quantifying point cloud realism through adversarially learned latent representations—0
An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time SeriesCode1
Multi-view Contrastive Self-Supervised Learning of Accounting Data Representations for Downstream Audit Tasks—0
DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security ApplicationsCode1
A Distance-based Anomaly Detection Framework for Deep Reinforcement Learning—0
Self-supervised Representation Learning for Reliable Robotic Monitoring of Fruit AnomaliesCode0
DeepTimeAnomalyViz: A Tool for Visualizing and Post-processing Deep Learning Anomaly Detection Results for Industrial Time-Series—0
A2Log: Attentive Augmented Log Anomaly Detection—0
Merlion: A Machine Learning Library for Time SeriesCode1
Anomaly Detection in Radar Data Using PointNets—0
Deep Quantile Regression for Uncertainty Estimation in Unsupervised and Supervised Lesion DetectionCode0
Deep Spatio-temporal Sparse Decomposition for Trend Prediction and Anomaly Detection in Cardiac Electrical Conduction—0
A Robust and Efficient Multi-Scale Seasonal-Trend Decomposition—0
3-Dimensional Deep Learning with Spatial Erasing for Unsupervised Anomaly Segmentation in Brain MRI—0
Challenging Current Semi-Supervised Anomaly Segmentation Methods for Brain MRICode1
Applications of Recurrent Neural Network for Biometric Authentication & Anomaly Detection—0
LEA-Net: Layer-wise External Attention Network for Efficient Color Anomaly Detection—0
A secondary immune response based on co-evolutive populations of agents for anomaly detection and characterization—0
Towards a Rigorous Evaluation of Time-series Anomaly DetectionCode1
PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT Data StreamsCode1
Enhancing Unsupervised Anomaly Detection with Score-Guided NetworkCode0
TENET: Temporal CNN with Attention for Anomaly Detection in Automotive Cyber-Physical Systems—0
Supervised Contrastive Learning for Detecting Anomalous Driving Behaviours from Multimodal VideosCode0
Detecting Attacks on IoT Devices using Featureless 1D-CNN—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