GearNet：弱监督领域自适应的分步对偶学习
GearNet: Stepwise Dual Learning for Weakly Supervised Domain Adaptation
摘要：
研究了弱监督领域自适应（WSDA）问题，即我们只能访问带有噪声标签的源域，而需要将有用信息从源域传递到未标记的目标域。虽然已有一些关于这个问题的研究，但大多数都是利用从源域到目标域的单向关系。在本文中，我们提出了一个通用的范式称为齿轮网利用双边关系的两个领域。具体地说，我们将这两个域作为不同的输入，交替地训练两个模型，并使用非对称Kullback-Leibler损失来选择性地匹配同一域中两个模型的预测。这种交互式学习模式使隐式标签噪声消除，并利用源域和目标域之间的相关性。因此，我们的GearNet有很大的潜力来提高各种现有WSDL方法的性能。综合实验结果表明，现有方法的性能可以显着提高装备我们的齿轮网络。
英文摘要：
This paper studies weakly supervised domain adaptation(WSDA) problem, where we only have access to the source domain with noisy labels, from which we need to transfer useful information to the unlabeled target domain. Although there have been a few studies on this problem, most of them only exploit unidirectional relationships from the source domain to the target domain. In this paper, we propose a universal paradigm called GearNet to exploit bilateral relationships between the two domains. Specifically, we take the two domains as different inputs to train two models alternately, and asymmetrical Kullback-Leibler loss is used for selectively matching the predictions of the two models in the same domain. This interactive learning schema enables implicit label noise canceling and exploits correlations between the source and target domains. Therefore, our GearNet has the great potential to boost the performance of a wide range of existing WSDL methods. Comprehensive experimental results show that the performance of existing methods can be significantly improved by equipping with our GearNet.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
