Boost Optical Flow with PD Data
Parallel Domain (PD) synthetic data improves performance on optical flow tasks by 18.5% by matching flow magnitudes.
Synthetic Data Best Practices for Perception Applications
Synthetic data best practices we learned from working with many perception teams and our internal research.
Not all Synthetic Datasets are Created Equal
Parallel Domain data improves unsupervised domain adaptation performance by 30% vs. GTA with no changes to model architecture.
Parallel Domain Raises $30 Million Series B to super-train AI
The company reimagining how AI learns, realizing an autonomous future for everyone and everything
Parallel Domain Synthetic Data Improves Cyclist Detection
Parallel Domain synthetic data significantly improves performance on rare classes such as bicycles with no changes to model architecture.
How Toyota Research Trains Better Computer Vision Models
Discussion with Toyota Research Institute’s Head of Machine Learning Research Adrien Gaidon
What is a Parallel Domain?
A Parallel Domain is a virtual world in which we generate synthetic data for training and testing.
