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 71–80 of 308 papers

TitleStatusHype
Communicating Smartly in the Molecular Domain: Neural Networks in the Internet of Bio-Nano ThingsCode0
From General to Targeted Rewards: Surpassing GPT-4 in Open-Ended Long-Context Generation—0
A large-scale, physically-based synthetic dataset for satellite pose estimation—0
Enhancing Clinical Models with Pseudo Data for De-identificationCode0
Code Execution as Grounded Supervision for LLM ReasoningCode0
Synthetic Dataset Generation for Autonomous Mobile Robots Using 3D Gaussian Splatting for Vision Training—0
Generating Synthetic Stereo Datasets using 3D Gaussian Splatting and Expert Knowledge Transfer—0
CETBench: A Novel Dataset constructed via Transformations over Programs for Benchmarking LLMs for Code-Equivalence Checking—0
Multi-Domain ABSA Conversation Dataset Generation via LLMs for Real-World Evaluation and Model Comparison—0
F-ANcGAN: An Attention-Enhanced Cycle Consistent Generative Adversarial Architecture for Synthetic Image Generation of NanoparticlesCode0
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