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

Showing 7180 of 308 papers

TitleStatusHype
Generating Traffic Scenarios via In-Context Learning to Learn Better Motion PlannerCode1
Movie2Story: A framework for understanding videos and telling stories in the form of novel text0
Cognition Chain for Explainable Psychological Stress Detection on Social MediaCode0
SciFaultyQA: Benchmarking LLMs on Faulty Science Question Detection with a GAN-Inspired Approach to Synthetic Dataset GenerationCode0
Unbiased General Annotated Dataset Generation0
VariFace: Fair and Diverse Synthetic Dataset Generation for Face Recognition0
JAPAGEN: Efficient Few/Zero-shot Learning via Japanese Training Dataset Generation with LLMCode0
SynFinTabs: A Dataset of Synthetic Financial Tables for Information and Table ExtractionCode0
An Evolutionary Large Language Model for Hallucination Mitigation0
SimuScope: Realistic Endoscopic Synthetic Dataset Generation through Surgical Simulation and Diffusion ModelsCode1
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