论文标题
具有成本效益的渐进域适应性框架的框架
Cost-effective Framework for Gradual Domain Adaptation with Multifidelity
论文作者
论文摘要
在域适应性中，当源和目标域之间存在很大距离时，预测性能将降低。假设我们可以访问中间域，从源逐渐从源转移到目标域，则逐渐的域适应性是解决此类问题的解决方案之一。在以前的工作中，假定中间域中的样品数量足够大。因此，无需标记数据就可以进行自我训练。如果限制了可访问的中间域的数量，则域之间的距离变大，自我训练将失败。实际上，中间域中样品的成本会有所不同，自然可以考虑到中间域越接近目标域，从中间域获得样品的成本就越高。为了解决成本和准确性之间的权衡，我们提出了一个结合了多重率和主动域适应性的框架。通过使用现实世界数据集的实验来评估所提出方法的有效性。
In domain adaptation, when there is a large distance between the source and target domains, the prediction performance will degrade. Gradual domain adaptation is one of the solutions to such an issue, assuming that we have access to intermediate domains, which shift gradually from the source to the target domain. In previous works, it was assumed that the number of samples in the intermediate domains was sufficiently large; hence, self-training was possible without the need for labeled data. If the number of accessible intermediate domains is restricted, the distances between domains become large, and self-training will fail. Practically, the cost of samples in intermediate domains will vary, and it is natural to consider that the closer an intermediate domain is to the target domain, the higher the cost of obtaining samples from the intermediate domain is. To solve the trade-off between cost and accuracy, we propose a framework that combines multifidelity and active domain adaptation. The effectiveness of the proposed method is evaluated by experiments with real-world datasets.
