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 281–290 of 308 papers

TitleStatusHype
Beyond Translation: LLM-Based Data Generation for Multilingual Fact-CheckingCode0
A universal synthetic dataset for machine learning on spectroscopic dataCode0
Zero-shot racially balanced dataset generation using an existing biased StyleGAN2Code0
F-ANcGAN: An Attention-Enhanced Cycle Consistent Generative Adversarial Architecture for Synthetic Image Generation of NanoparticlesCode0
PAXQA: Generating Cross-lingual Question Answering Examples at Training ScaleCode0
Face Manifold: Manifold Learning for Synthetic Face GenerationCode0
Evaluating ML-Based Anomaly Detection Across Datasets of Varied Integrity: A Case StudyCode0
Enhancing Clinical Models with Pseudo Data for De-identificationCode0
Pipeline and Dataset Generation for Automated Fact-checking in Almost Any LanguageCode0
Private Dataset Generation Using Privacy Preserving Collaborative LearningCode0
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