We present a novel instance based approach to handle regression tasks in the context of supervised domain adaptation. The approach developed in this paper relies on the assumption that the task on the target domain can be efficiently learned by adequately reweighting the source instances during training phase. We introduce a novel formulation of the optimization objective for domain adaptation which relies on a discrepancy distance characterizing the difference between domains according to a specific task and a class of hypotheses. To solve this problem, we develop an adversarial network algorithm which learns both the source weighting scheme and the task in one feed-forward gradient descent. We provide numerical evidence of the relevance of the method on public datasets for domain adaptation through reproducible experiments accessible via an online demo interface.

翻译：本文所制定的方法所依据的假设是,目标领域的任务可以通过在培训阶段对源实例进行充分的重新加权来有效学习。我们引入了领域适应优化目标新颖的提法,它依赖于根据具体任务和一系列假设区分不同领域之间的差异。为了解决这一问题,我们开发了一种对抗性网络算法,既学习源加权办法,又学习一次反馈前梯度下降的任务。我们提供了数字证据,说明通过在线演示界面可复制的实验,说明用于领域适应的公共数据集方法的相关性。