  Recent progress in material data mining has been driven by high-capacity
models trained on large datasets. However, collecting experimental data (real
data) has been extremely costly since the amount of human effort and expertise
required. Here, we develop a novel transfer learning strategy to address small
or insufficient data problem. This strategy realizes the fusion of real and
simulated data, and the augmentation of training data in data mining procedure.
For a specific task of image segmentation, this strategy can generate synthetic
images by fusing physical mechanism of simulated images and "image style" of
real images. The result shows that the model trained with the acquired
synthetic images and 35% of the real images outperforms the model trained on
all real images. As the time required to generate synthetic data is almost
negligible, this strategy is able to reduce the time cost of real data
preparation by roughly 65%.
