Robuta

GCC Synthetic Data Generation Market Is Projected To Grow from USD 15.41 Million to USD 692.36 Million by 2035, Reaching at a CAGR of 46.31% During 2025 - 2035

Synthetic Data Generation Market is Estimated to Reach USD 34.62 Billion By 2035, Growing at a CAGR of 46.30% During 2025 - 2035 | Demand For Diverse Datasets

A reference application enabling developers to design, simulate, test, and train AI-based robots in a physically-based virtual environment.

Abstract page for arXiv paper 2412.16207v1: Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study

Abstract page for arXiv paper 2502.16932: DemoGen: Synthetic Demonstration Generation for Data-Efficient Visuomotor Policy Learning

Synthetic Data Generation is the process of creating artificial datasets that mimic the statistical properties and characteristics of real-world data without...

Research into advanced manufacturing requires data for analysis.

Artificial intelligence (AI) is widely used in healthcare applications to perform various tasks. Although these models have great potential to improve the...

We present CALICO, a method to fine-tune Large Language Models (LLMs) to localize conversational agent training data from one language to another. For slots...

Synthetic data generation for vision market to reach $5.19 billion by 2030 at 27.7% cagr, driven by rising adoption of cloud-based solutions.

Build SDG pipelines to power conversational AI, benchmarks, and agentic AI workflows with NVIDIA NeMo synthetic data tools.

This paper provides a methodology for the production of synthetic images for training neural networks to recognise shapes and objects. There are many scenarios...

Distilabel is an AI Feedback (AIF) framework for building datasets with and for LLMs.

Text-to-image (T2I) models are increasingly used for synthetic dataset generation, but generating synthetic training data to improve fine-grained...