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 201–225 of 308 papers

TitleStatusHype
Synthetic Dataset Generation with Itemset-Based Generative Models—0
Synthetic Datasets for Autonomous Driving: A Survey—0
Synthetic Error Dataset Generation Mimicking Bengali Writing Pattern—0
Synthetic-to-real Composite Semantic Segmentation in Additive Manufacturing—0
Synthetic User Behavior Sequence Generation with Large Language Models for Smart Homes—0
Technical report of a DMD-based Characterization Method for Vision Sensors—0
The Big Data Myth: Using Diffusion Models for Dataset Generation to Train Deep Detection Models—0
Through Fog High-Resolution Imaging Using Millimeter Wave Radar—0
Towards a methodology for addressing missingness in datasets, with an application to demographic health datasets—0
Towards Real-World Category-level Articulation Pose Estimation—0
Training dataset generation for bridge game registration—0
TrainSim: A Railway Simulation Framework for LiDAR and Camera Dataset Generation—0
Transfer learning for self-supervised, blind-spot seismic denoising—0
Unbiased General Annotated Dataset Generation—0
Undertrained Image Reconstruction for Realistic Degradation in Blind Image Super-Resolution—0
Unreal Robotics Lab: A High-Fidelity Robotics Simulator with Advanced Physics and Rendering—0
Unsupervised Learning of Shape Concepts - From Real-World Objects to Mental Simulation—0
Unsupervised Multi-label Dataset Generation from Web Data—0
The Invisible Hand: Unveiling Provider Bias in Large Language Models for Code Generation—0
Unveiling the Invisible: Reasoning Complex Occlusions Amodally with AURA—0
USM-SED - A Dataset for Polyphonic Sound Event Detection in Urban Sound Monitoring Scenarios—0
V^2R-Bench: Holistically Evaluating LVLM Robustness to Fundamental Visual Variations—0
ValuePilot: A Two-Phase Framework for Value-Driven Decision-Making—0
VariFace: Fair and Diverse Synthetic Dataset Generation for Face Recognition—0
Vec2Face: Scaling Face Dataset Generation with Loosely Constrained Vectors—0
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