CVPR 2023 - A New Benchmark: On the Utility of Synthetic Data with Blender for

In this episode we discuss A New Benchmark: On the Utility of Synthetic Data with Blender for by Authors: Hui Tang and Kui Jia. The paper discusses the limitations of deep learning in computer vision due to the need for large-scale labeled training data and the impracticality of exhaustive data annotation. To address this, the authors propose generating synthetic data via 3D rendering with domain randomization. Through their research, they systematically verify important learning insights and discover new laws of various data regimes and network architectures in generalization. They also investigate the effect of image formation factors on generalization and use simulation-to-reality adaptation as a downstream task for comparing the transferability between synthetic and real data for pre-training. Finally, they develop a new benchmark for image classification, S2RDA, to provide more significant challenges for transfer from simulation to reality.