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 41–50 of 308 papers

TitleStatusHype
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
Learning-based NLOS Detection and Uncertainty Prediction of GNSS Observations with Transformer-Enhanced LSTM NetworkCode1
DiffuGen: Adaptable Approach for Generating Labeled Image Datasets using Stable Diffusion ModelsCode1
Developing a Scalable Benchmark for Assessing Large Language Models in Knowledge Graph EngineeringCode1
Supervised Homography Learning with Realistic Dataset GenerationCode1
SynTable: A Synthetic Data Generation Pipeline for Unseen Object Amodal Instance Segmentation of Cluttered Tabletop ScenesCode1
NeuroGraph: Benchmarks for Graph Machine Learning in Brain ConnectomicsCode1
Sim-Suction: Learning a Suction Grasp Policy for Cluttered Environments Using a Synthetic BenchmarkCode1
Goat: Fine-tuned LLaMA Outperforms GPT-4 on Arithmetic TasksCode1
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