Is Synthetic Image Useful for Transfer Learning? An Investigation into Data Generation, Volume, and Utilization

arXiv:2403.19866v1 Announce Type: new
Abstract: Synthetic image data generation represents a promising avenue for training deep learning models, particularly in the realm of transfer learning, where obtaining real images within a specific domain can be prohibitively expensive due to privacy and intellectual property considerations. This work delves into the generation and utilization of synthetic images derived from text-to-image generative models in facilitating transfer learning paradigms. Despite the high visual fidelity of the generated images, we observe that their naive incorporation …

abstract arxiv cs.ai cs.cv data deep learning domain image image data images intellectual property investigation privacy property synthetic training transfer transfer learning type