请翻译：传统的虹膜分类研究中我们往往假设训练集和测试集分布一致在训练集上训练模型在测试集上测试。然而在实际问题中测试场景往往非可控测试集和训练集分布有很大差异这时候就会出现所谓过拟合问题模型在测试集上效果不理想。当训练集和测试集分布不一致的情况下例如换设备、终端用户、大量未出现过的美瞳类型可以通过迁移学习技术实现快速敏捷部署。领域自适应Domain Adaptation是迁移学习中的一种代表性方法

In traditional iris classification research, we often assume that the distribution of the training set and the test set is consistent, and we train the model on the training set and test it on the test set. However, in practical scenarios, the test scene is often uncontrollable, and there is a significant difference in the distribution between the test set and the training set, which can lead to overfitting problems and the model performs poorly on the test set. When the distribution of the training set and the test set is inconsistent, such as changing devices, end users, and a large number of unobserved contact lens types, agile deployment can be achieved through transfer learning technology. Domain adaptation is a representative method in transfer learning, which refers to using information-rich samples from the source domain to improve the performance of the target domain model. The source domain represents a different domain from the test sample, but has rich supervision information; the target domain represents the domain where the test sample is located, with few or no labels. The source domain and the target domain often belong to the same task, but have different distributions