On the Equivalency, Substitutability, and Flexibility of Synthetic Data

arXiv:2403.16244v1 Announce Type: new
Abstract: We study, from an empirical standpoint, the efficacy of synthetic data in real-world scenarios. Leveraging synthetic data for training perception models has become a key strategy embraced by the community due to its efficiency, scalability, perfect annotations, and low costs. Despite proven advantages, few studies put their stress on how to efficiently generate synthetic datasets to solve real-world problems and to what extent synthetic data can reduce the effort for real-world data collection. To answer …

abstract advantages annotations arxiv become community costs cs.cv cs.lg data efficiency flexibility key low perception scalability strategy studies study synthetic synthetic data training type world