Integrate model results and automatically generate data to address failure cases. Rapidly target data sparsity issues, class imbalances, and other biases with scalable synthetic data generation.
Programmatically generate and train with dense labels like semantic segmentation, depth, and optical flow, which are expensive or impossible to obtain for real data.
Rapidly expand to new regions, classes, or sensor hardware by utilizing synthetic data for transfer learning. Use region-specific assets such as traffic signs to safely expand operations with less reliance on real data.
Improve the efficiency and accuracy of manual data labeling by training auto labelers with synthetic data. Utilize labeled synthetic data to kickstart semi-supervised learning on real data.
Applied’s cloud-first tools help users define, generate, and manage data easily across their ML team.