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 3401–3450 of 4856 papers

TitleStatusHype
A Comprehensive Study of Machine Learning Techniques for Log-Based Anomaly Detection—0
An Encoder-Decoder Based Approach for Anomaly Detection with Application in Additive Manufacturing—0
An Energy Consumption Model for Electrical Vehicle Networks via Extended Federated-learning—0
An Energy Efficient Health Monitoring Approach with Wireless Body Area Networks—0
An Ensemble Approach toward Automated Variable Selection for Network Anomaly Detection—0
A Neural Network Anomaly Detector Using the Random Cluster Model—0
A neural-network based anomaly detection system and a safety protocol to protect vehicular network—0
A Neural Network-Based On-device Learning Anomaly Detector for Edge Devices—0
A Neural Network for Determination of Latent Dimensionality in Nonnegative Matrix Factorization—0
A Neuro-Symbolic Explainer for Rare Events: A Case Study on Predictive Maintenance—0
An Event based Prediction Suffix Tree—0
A New Approach to Dimensionality Reduction for Anomaly Detection in Data Traffic—0
A New Comprehensive Benchmark for Semi-supervised Video Anomaly Detection and Anticipation—0
A new GAN-based anomaly detection (GBAD) approach for multi-threat object classification on large-scale x-ray security images—0
A new interpretable unsupervised anomaly detection method based on residual explanation—0
Changes to the extreme and erratic behaviour of cryptocurrencies during COVID-19—0
A New Perspective on Time Series Anomaly Detection: Faster Patch-based Broad Learning System—0
A New Time Series Similarity Measure and Its Smart Grid Applications—0
A new Video Synopsis Based Approach Using Stereo Camera—0
An Expert Ensemble for Detecting Anomalous Scenes, Interactions, and Behaviors in Autonomous Driving—0
An Explainable Anomaly Detection Framework for Monitoring Depression and Anxiety Using Consumer Wearable Devices—0
An Explainable Artificial Intelligence Approach for Unsupervised Fault Detection and Diagnosis in Rotating Machinery—0
An Explainable Deep Learning Framework for Brain Stroke and Tumor Progression via MRI Interpretation—0
An Improvement of PAA on Trend-Based Approximation for Time Series—0
An Incremental Clustering Method for Anomaly Detection in Flight Data—0
An Input-to-State Safety Approach Towards Safe Control of a Class of Parabolic PDEs Under Disturbances—0
An Introduction to Autoencoders—0
An Iterative Method for Unsupervised Robust Anomaly Detection Under Data Contamination—0
An LSTM-Based Predictive Monitoring Method for Data with Time-varying Variability—0
AnoDODE: Anomaly Detection with Diffusion ODE—0
An Off-the-shelf Approach to Authorship Attribution—0
AnoGAN for Tabular Data: A Novel Approach to Anomaly Detection—0
Ano-Graph: Learning Normal Scene Contextual Graphs to Detect Video Anomalies—0
Anomalib: A Deep Learning Library for Anomaly Detection—0
Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation—0
Anomalies, Representations, and Self-Supervision—0
Anomalous Change Point Detection Using Probabilistic Predictive Coding—0
Anomalous Client Detection in Federated Learning—0
Anomalous Example Detection in Deep Learning: A Survey—0
AnomalousPatchCore: Exploring the Use of Anomalous Samples in Industrial Anomaly Detection—0
Anomalous Sound Detection Based on Machine Activity Detection—0
Anomalous Sound Detection using Audio Representation with Machine ID based Contrastive Learning Pretraining—0
Anomalous State Sequence Modeling to Enhance Safety in Reinforcement Learning—0
Anomaly and Change Detection in Graph Streams through Constant-Curvature Manifold Embeddings—0
Anomaly and Fraud Detection in Credit Card Transactions Using the ARIMA Model—0
Anomaly Awareness—0
Anomaly-Aware Semantic Segmentation by Leveraging Synthetic-Unknown Data—0
Anomaly-Aware Semantic Segmentation via Style-Aligned OoD Augmentation—0
Abnormal component analysis—0
Anomaly Correction of Business Processes Using Transformer Autoencoder—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