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 1251–1275 of 4856 papers

TitleStatusHype
A Scalable Algorithm for Anomaly Detection via Learning-Based Controlled Sensing—0
An Off-the-shelf Approach to Authorship Attribution—0
Advancing Video Anomaly Detection: A Bi-Directional Hybrid Framework for Enhanced Single- and Multi-Task Approaches—0
Artificial intelligence for abnormality detection in high volume neuroimaging: a systematic review and meta-analysis—0
Artificial Intelligence and Machine Learning in 5G Network Security: Opportunities, advantages, and future research trends—0
Arrays of (locality-sensitive) Count Estimators (ACE): High-Speed Anomaly Detection via Cache Lookups—0
AnoDODE: Anomaly Detection with Diffusion ODE—0
Acoustic anomaly detection via latent regularized gaussian mixture generative adversarial networks—0
A Robust Likelihood Model for Novelty Detection—0
Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data—0
A Robust Autoencoder Ensemble-Based Approach for Anomaly Detection in Text—0
A Robust and Explainable Data-Driven Anomaly Detection Approach For Power Electronics—0
An LSTM-Based Predictive Monitoring Method for Data with Time-varying Variability—0
Acoustic Anomaly Detection for Machine Sounds based on Image Transfer Learning—0
A Robust and Efficient Multi-Scale Seasonal-Trend Decomposition—0
A Roadmap Towards Resilient Internet of Things for Cyber-Physical Systems—0
An Iterative Method for Unsupervised Robust Anomaly Detection Under Data Contamination—0
Identifying Backdoor Attacks in Federated Learning via Anomaly Detection—0
Argos: Agentic Time-Series Anomaly Detection with Autonomous Rule Generation via Large Language Models—0
An Introduction to Autoencoders—0
Integrating Graph Neural Networks with Scattering Transform for Anomaly Detection—0
ABCD: Trust enhanced Attention based Convolutional Autoencoder for Risk Assessment—0
A convolutional neural network of low complexity for tumor anomaly detection—0
An Input-to-State Safety Approach Towards Safe Control of a Class of Parabolic PDEs Under Disturbances—0
Deep 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