用学术英语来翻译下面这段话：其中表示源域的样本表示源域的标签6中表示目标域的样本表示目标域的标签一般情况下源域的样本数量是要远大于目标域的情况下会使用迁移学习的方法来为目标域的模型引入源域的知识当然如果数量差不多的情况下也是可以考虑使用迁移学习来尝试增强目标域模型的性能的。在得到训练好的模型后常采用迁移学习将模型应用到其他任务中迁移学习的步骤为：在初次训练的基础上对额外的新数据再训练微调网络参数。

The samples representing the source domain and their corresponding labels, as well as the samples representing the target domain and their corresponding labels (indicated by (6)), are considered. In general, the number of samples in the source domain is much larger than that in the target domain, and as such, transfer learning methods are often used to introduce knowledge from the source domain to the target domain model. However, even when the sample sizes are comparable, transfer learning can still be considered to enhance the performance of the target domain model. After obtaining a well-trained model, transfer learning is commonly used to apply the model to other tasks. The steps of transfer learning involve further training the model with additional new data while fine-tuning network parameters based on the initial training. This approach saves a significant amount of computational resources and time by avoiding the need to train the network from scratch. During the fine-tuning process with new data, it is often necessary to freeze most of the network layers. In this study, the first two layers without the CBAM module were frozen to avoid overfitting, and the specific operational procedure is depicted in Figure 5