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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3351–3375 of 4856 papers

TitleStatusHype
A Massively Parallel Associative Memory Based on Sparse Neural Networks—0
An Investigation of Traffic Density Changes inside Wuhan during the COVID-19 Epidemic with GF-2 Time-Series Images—0
A Method for Detecting Abnormal Data of Network Nodes Based on Convolutional Neural Network—0
A method for incremental discovery of financial event types based on anomaly detection—0
A Methodological Report on Anomaly Detection on Dynamic Knowledge Graphs—0
A Methodology for the Diagnostic of Aircraft Engine Based on Indicators Aggregation—0
Am I Rare? An Intelligent Summarization Approach for Identifying Hidden Anomalies—0
‘Am I the Bad One’? Predicting the Moral Judgement of the Crowd Using Pre–trained Language Models—0
A Model-Free Kullback-Leibler Divergence Filter for Anomaly Detection in Noisy Data Series—0
A Modified Dynamic Time Warping (MDTW) Approach and Innovative Average Non-Self Match Distance (ANSD) Method for Anomaly Detection in ECG Recordings—0
A Modular and Unified Framework for Detecting and Localizing Video Anomalies—0
Grading and Anomaly Detection for Automated Retinal Image Analysis using Deep Learning—0
A multi-domain splitting framework for time-varying graph structure—0
A Multi-Level Approach for Class Imbalance Problem in Federated Learning for Remote Industry 4.0 Applications—0
A Multi-modal one-class generative adversarial network for anomaly detection in manufacturing—0
A multi-model approach using XAI and anomaly detection to predict asteroid hazards—0
A Multi-perspective Approach To Anomaly Detection For Self-aware Embodied Agents—0
A Multi-Scale A Contrario method for Unsupervised Image Anomaly Detection—0
A Multi-Step Comparative Framework for Anomaly Detection in IoT Data Streams—0
A multi-stream deep neural network with late fuzzy fusion for real-world anomaly detection—0
A Multi-View Framework for BGP Anomaly Detection via Graph Attention Network—0
An Adaptive Approach for Anomaly Detector Selection and Fine-Tuning in Time Series—0
An Adaptive Event-based Data Converter for Always-on Biomedical Applications at the Edge—0
An ADMM-based Optimal Transmission Frequency Management System for IoT Edge Intelligence—0
An AI-Based Public Health Data Monitoring System—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