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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 181–190 of 308 papers

TitleStatusHype
LLMaAA: Making Large Language Models as Active AnnotatorsCode1
On the Inherent Privacy Properties of Discrete Denoising Diffusion Models—0
Location-Aware Visual Question Generation with Lightweight ModelsCode0
AutoHall: Automated Hallucination Dataset Generation for Large Language Models—0
Dataset Diffusion: Diffusion-based Synthetic Dataset Generation for Pixel-Level Semantic SegmentationCode1
Fabricator: An Open Source Toolkit for Generating Labeled Training Data with Teacher LLMsCode1
Dataset Generation and Bonobo Classification from Weakly Labelled VideosCode0
DiffuGen: Adaptable Approach for Generating Labeled Image Datasets using Stable Diffusion ModelsCode1
Learning-based NLOS Detection and Uncertainty Prediction of GNSS Observations with Transformer-Enhanced LSTM NetworkCode1
Developing a Scalable Benchmark for Assessing Large Language Models in Knowledge Graph EngineeringCode1
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