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
Physics Informed Distillation for Diffusion ModelsCode2
DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion ModelsCode2
JAX-SPH: A Differentiable Smoothed Particle Hydrodynamics FrameworkCode2
MultiCorrupt: A Multi-Modal Robustness Dataset and Benchmark of LiDAR-Camera Fusion for 3D Object DetectionCode2
Vision Language Action Models in Robotic Manipulation: A Systematic ReviewCode2
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
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
Cephalo: Multi-Modal Vision-Language Models for Bio-Inspired Materials Analysis and DesignCode1
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