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 3701–3725 of 4856 papers

TitleStatusHype
A Rank-SVM Approach to Anomaly Detection—0
Arbitrary Discrete Sequence Anomaly Detection with Zero Boundary LSTM—0
ARCADE: Adversarially Regularized Convolutional Autoencoder for Network Anomaly Detection—0
Architecture of Data Anomaly Detection-Enhanced Decentralized Expert System for Early-Stage Alzheimer's Disease Prediction—0
A Real-time Anomaly Detection Using Convolutional Autoencoder with Dynamic Threshold—0
Are Anomaly Scores Telling the Whole Story? A Benchmark for Multilevel Anomaly Detection—0
A Reconfigurable Low Power High Throughput Architecture for Deep Network Training—0
A Recover-then-Discriminate Framework for Robust Anomaly Detection—0
Are Large Language Models Useful for Time Series Data Analysis?—0
A Reliable Framework for Human-in-the-Loop Anomaly Detection in Time Series—0
A Representation Learning Approach to Feature Drift Detection in Wireless Networks—0
Are pre-trained CNNs good feature extractors for anomaly detection in surveillance videos?—0
A Review of Computer Vision Methods in Network Security—0
A Review of Machine Learning based Anomaly Detection Techniques—0
A review of neural network algorithms and their applications in supercritical extraction—0
A Review of Open Source Software Tools for Time Series Analysis—0
A Review of Physics-Informed Machine Learning Methods with Applications to Condition Monitoring and Anomaly Detection—0
A review on outlier/anomaly detection in time series data—0
Are vision language models robust to uncertain inputs?—0
Argos: Agentic Time-Series Anomaly Detection with Autonomous Rule Generation via Large Language Models—0
Identifying Backdoor Attacks in Federated Learning via Anomaly Detection—0
A Roadmap Towards Resilient Internet of Things for Cyber-Physical Systems—0
A Robust and Efficient Multi-Scale Seasonal-Trend Decomposition—0
A Robust and Explainable Data-Driven Anomaly Detection Approach For Power Electronics—0
A Robust Autoencoder Ensemble-Based Approach for Anomaly Detection in Text—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