说明：  本文介绍了一种深度迁移学习方法，称为选择性联合微调，用于在训练数据不足的情况下提高深度学习任务的性能。
(In this paper, we introduce a deep transfer learning scheme, called selective joint fine-tuning, for improving the performance of deep learning tasks with insufficient training data.)