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 3151–3175 of 4856 papers

TitleStatusHype
A2Log: Attentive Augmented Log Anomaly Detection—0
AAD: Adaptive Anomaly Detection through traffic surveillance videos—0
AAD-LLM: Adaptive Anomaly Detection Using Large Language Models—0
A Bayesian Ensemble for Unsupervised Anomaly Detection—0
A Bayesian Framework for Digital Twin-Based Control, Monitoring, and Data Collection in Wireless Systems—0
A Bayesian Non-parametric Approach to Generative Models: Integrating Variational Autoencoder and Generative Adversarial Networks using Wasserstein and Maximum Mean Discrepancy—0
ABBA: Adaptive Brownian bridge-based symbolic aggregation of time series—0
ABCD: Trust enhanced Attention based Convolutional Autoencoder for Risk Assessment—0
A Benchmark dataset for predictive maintenance—0
A Bi-LSTM Autoencoder Framework for Anomaly Detection -- A Case Study of a Wind Power Dataset—0
Abnormal activity capture from passenger flow of elevator based on unsupervised learning and fine-grained multi-label recognition—0
Abnormal-aware Multi-person Evaluation System with Improved Fuzzy Weighting—0
Abnormal Client Behavior Detection in Federated Learning—0
Abnormal Event Detection in Videos using Generative Adversarial Nets—0
Abnormal Event Detection In Videos Using Deep Embedding—0
Abnormality Detection and Localization in Chest X-Rays using Deep Convolutional Neural Networks—0
Abnormality Detection in Mammography using Deep Convolutional Neural Networks—0
Abnormality Detection in Musculoskeletal Radiographs with Convolutional Neural Networks(Ensembles) and Performance Optimization—0
Abnormal Object Recognition: A Comprehensive Study—0
Abnormal Road Surface Detection Using Wheel Sensor Data—0
A Promotion Method for Generation Error Based Video Anomaly Detection—0
Absolute-Unified Multi-Class Anomaly Detection via Class-Agnostic Distribution Alignment—0
Abuse and Fraud Detection in Streaming Services Using Heuristic-Aware Machine Learning—0
Protecting Federated Learning from Extreme Model Poisoning Attacks via Multidimensional Time Series Anomaly Detection—0
A Case for the Score: Identifying Image Anomalies using Variational Autoencoder 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