标题： 通过Mixup训练改进无监督域适应 显示英文标题

标题： Improve Unsupervised Domain Adaptation with Mixup Training

摘要： 无监督域适应研究利用相关源域的丰富标签来构建未标注目标域的预测模型的问题。 最近的工作观察到，学习域不变特征的流行对抗方法不足以实现理想的目标域性能，因此引入了额外的训练约束，例如聚类假设。 然而，这些方法分别在源域和目标域上施加约束，忽略了它们之间的重要相互作用。 在本工作中，我们提出使用mixup公式在域间施加训练约束，以直接解决目标数据的泛化性能。 为了应对潜在的巨大域差异，我们进一步提出了一种特征级一致性正则化器，以促进域间约束。 当添加域内mixup和域对抗学习时，我们的通用框架在图像分类和人体活动识别的几个重要任务上显著提高了最先进的性能。 显示英文摘要

摘要： Unsupervised domain adaptation studies the problem of utilizing a relevant source domain with abundant labels to build predictive modeling for an unannotated target domain. Recent work observe that the popular adversarial approach of learning domain-invariant features is insufficient to achieve desirable target domain performance and thus introduce additional training constraints, e.g. cluster assumption. However, these approaches impose the constraints on source and target domains individually, ignoring the important interplay between them. In this work, we propose to enforce training constraints across domains using mixup formulation to directly address the generalization performance for target data. In order to tackle potentially huge domain discrepancy, we further propose a feature-level consistency regularizer to facilitate the inter-domain constraint. When adding intra-domain mixup and domain adversarial learning, our general framework significantly improves state-of-the-art performance on several important tasks from both image classification and human activity recognition.