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 1120 of 308 papers

TitleStatusHype
JAX-SPH: A Differentiable Smoothed Particle Hydrodynamics FrameworkCode2
DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion ModelsCode2
DataDream: Few-shot Guided Dataset GenerationCode2
MultiCorrupt: A Multi-Modal Robustness Dataset and Benchmark of LiDAR-Camera Fusion for 3D Object DetectionCode2
UniGen: A Unified Framework for Textual Dataset Generation Using Large Language ModelsCode2
CySecBench: Generative AI-based CyberSecurity-focused Prompt Dataset for Benchmarking Large Language ModelsCode1
Afro-MNIST: Synthetic generation of MNIST-style datasets for low-resource languagesCode1
Detecting Anti-Vaccine Users on TwitterCode1
CRAFT Your Dataset: Task-Specific Synthetic Dataset Generation Through Corpus Retrieval and AugmentationCode1
Dataset Diffusion: Diffusion-based Synthetic Dataset Generation for Pixel-Level Semantic SegmentationCode1
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