Meta-Sim: Learning to Generate Synthetic Datasets

Understand

Training models to high-end performance requires availability of large labeled datasets, which are expensive to get.

• The goal of our work is to automatically synthesize labeled datasets that are relevant for a downstream task.
• We propose Meta-Sim, which learns a generative model of synthetic scenes, and obtain images as well as its corresponding ground-truth via a graphics engine.
• We parametrize our dataset generator with a neural network, which learns to modify attributes of scene graphs obtained from probabilistic scene grammars, so as to minimize the distribution gap between its rendered outputs and target data.