Domain adaptation is an important branch of transfer learning. Previous studies have always taken efforts to minimize the optimization goal, but they neglect the relative quality of features or instances. For example, a classic work treats different instances equally in a degree and chooses these instances which minimize the optimization function value. This method will discard these instances that make the data distribution in source and target data domain different and will neglect the instances’ relative quality. To reduce interference between instances in the process of domain adaptation, we put forward a novel method of ODA that uses the overlapping degree to measure every feature or instance’s relative quality and implement feature or instance reweighting. At the same time, we have noticed that there are many parameters with values that will influence the effect of the method. Previous studies do not have a reasonable method to determine the parameters’ values. We can use the genetic algorithm to find the balance between marginal distribution adaptation and conditional distribution adaptation to find the best combination of multiple parameters. Experiments we have done verify that the ODA method outperforms by 3.26% compared with the best comparison method. We have found that our method of finding the optimal parameters can yield more accurate results than the original method.

中文翻译：

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有偏转移匹配的重叠程度较小，可实现无监督域自适应

领域适应是迁移学习的重要分支。以前的研究一直在努力使优化目标最小化，但忽略了要素或实例的相对质量。例如，经典作品在一定程度上均等地对待不同的实例，并选择这些实例以最小化优化函数值。此方法将丢弃这些实例，这些实例会使源数据域和目标数据域中的数据分布不同，并且会忽略实例的相对质量。为了减少域自适应过程中实例之间的干扰，我们提出了一种新的ODA方法，该方法使用重叠度来度量每个特征或实例的相对质量并实现特征或实例的加权。同时，我们注意到，有许多参数的值会影响该方法的效果。先前的研究没有确定参数值的合理方法。我们可以使用遗传算法找到边际分布自适应和条件分布自适应之间的平衡，以找到多个参数的最佳组合。我们所做的实验证实，与最佳比较方法相比，ODA方法的性能要高出3.26％。我们发现，寻找最佳参数的方法比原始方法可以产生更准确的结果。我们可以使用遗传算法找到边际分布自适应和条件分布自适应之间的平衡，以找到多个参数的最佳组合。我们所做的实验证实，与最佳比较方法相比，ODA方法的性能要高出3.26％。我们发现，寻找最佳参数的方法比原始方法可以产生更准确的结果。我们可以使用遗传算法找到边际分布自适应和条件分布自适应之间的平衡，以找到多个参数的最佳组合。我们所做的实验证实，与最佳比较方法相比，ODA方法的性能要高出3.26％。我们发现，寻找最佳参数的方法比原始方法可以产生更准确的结果。