论文标题
潜在领域适应的快速OT
Fast OT for Latent Domain Adaptation
论文作者
论文摘要
在本文中，我们解决了无监督域适应的问题。当目标数据的分布与用于开发模型的数据和目标数据的基础真实信息的分布不同时，就会出现这种适应性。我们提出了一种算法，该算法使用最佳运输理论具有可靠的有效和可实现的解决方案来学习最佳的潜在特征表示。这是通过最大程度地减少将样品从目标域运输到源域分布的成本来实现的。
In this paper, we address the problem of unsupervised Domain Adaptation. The need for such an adaptation arises when the distribution of the target data differs from that which is used to develop the model and the ground truth information of the target data is unknown. We propose an algorithm that uses optimal transport theory with a verifiably efficient and implementable solution to learn the best latent feature representation. This is achieved by minimizing the cost of transporting the samples from the target domain to the distribution of the source domain.
