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 3301–3325 of 4856 papers

TitleStatusHype
Anomaly Detection using Capsule Networks for High-dimensional Datasets—0
Dense Out-of-Distribution Detection by Robust Learning on Synthetic Negative Data—0
A Multi-View Framework for BGP Anomaly Detection via Graph Attention Network—0
Bottom-up approaches for multi-person pose estimation and it's applications: A brief review—0
Unsupervised deep learning techniques for powdery mildew recognition based on multispectral imaging—0
Projected Sliced Wasserstein Autoencoder-based Hyperspectral Images Anomaly Detection—0
A Deep Learning Approach for Ontology Enrichment from Unstructured Text—0
Real-time Detection of Anomalies in Multivariate Time Series of Astronomical Data—0
Approaches Toward Physical and General Video Anomaly DetectionCode0
Utilizing XAI technique to improve autoencoder based model for computer network anomaly detection with shapley additive explanation(SHAP)—0
Noise Reduction and Driving Event Extraction Method for Performance Improvement on Driving Noise-based Surface Anomaly Detection—0
Out-of-Distribution Detection Without Class Labels—0
Deep learning-based defect detection of metal parts: evaluating current methods in complex conditions—0
Why Are You Weird? Infusing Interpretability in Isolation Forest for Anomaly DetectionCode0
Anomaly Crossing: New Horizons for Video Anomaly Detection as Cross-domain Few-shot Learning—0
DeepFIB: Self-Imputation for Time Series Anomaly Detection—0
Multimedia Datasets for Anomaly Detection: A Review—0
Fast and scalable neuroevolution deep learning architecture search for multivariate anomaly detection—0
Discrete neural representations for explainable anomaly detection—0
Ymir: A Supervised Ensemble Framework for Multivariate Time Series Anomaly Detection—0
A Hierarchical Spatio-Temporal Graph Convolutional Neural Network for Anomaly Detection in Videos—0
Scalable and Decentralized Algorithms for Anomaly Detection via Learning-Based Controlled Sensing—0
Smart Metering System Capable of Anomaly Detection by Bi-directional LSTM Autoencoder—0
Online false discovery rate control for anomaly detection in time series—0
Autoencoders for Semivisible Jet Detection—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