SOTAVerified

Dataset Generation

The task involves enhancing the training of target application (e.g. autonomous driving systems) by generating datasets of diverse and critical elements (e.g. traffic scenarios). Traditional methods rely on expensive and limited datasets, which often fail to capture rare but essential situations that can pose risks during testing.

Papers

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Showing 276–300 of 308 papers

TitleStatusHype
Synthetic Dataset Generation of Driver TelematicsCode0
Do Transformers Understand Polynomial Simplification?—0
Radar Artifact Labeling Framework (RALF): Method for Plausible Radar Detections in Datasets—0
Exploration of Cross-lingual Summarization for Kannada-EnglishLanguage Pair—0
Exploring the Impacts from Datasets to Monocular Depth Estimation (MDE) Models with MineNavi—0
Synthetic Dataset Generation with Itemset-Based Generative Models—0
Through Fog High-Resolution Imaging Using Millimeter Wave Radar—0
Learning Camera Miscalibration DetectionCode0
Unsupervised Multi-label Dataset Generation from Web Data—0
On Deep Learning for Radio Resource Management in A Non-stationary Radio Environment—0
Private Dataset Generation Using Privacy Preserving Collaborative LearningCode0
Synthetic Error Dataset Generation Mimicking Bengali Writing Pattern—0
Smart Home Appliances: Chat with Your FridgeCode0
Face Manifold: Manifold Learning for Synthetic Face GenerationCode0
Cut-and-Paste Dataset Generation for Balancing Domain Gaps in Object Instance Detection—0
Synthetic dataset generation for object-to-model deep learning in industrial applicationsCode0
Learning to Propagate for Graph Meta-LearningCode0
GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative ModelsCode0
Methodology for the Automated Metadata-Based Classification of Incriminating Digital Forensic Artefacts—0
Selection of Waveform Parameters Using Machine Learning for 5G and Beyond—0
Rethinking Table Recognition using Graph Neural NetworksCode0
LeagueAI: Improving object detector performance and flexibility through automatically generated training data and domain randomizationCode0
Fonts-2-Handwriting: A Seed-Augment-Train framework for universal digit classificationCode0
Boundary Aware Multi-Focus Image Fusion Using Deep Neural Network—0
A Seed-Augment-Train Framework for Universal Digit Classification—0
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