In this paper, we address the problem of unsupervised domain adaptation in a regression setting considering that source data have different representations (multiple views). In this work, we investigate an original method which takes advantage of different representations using a discrepancy distance while using attention-based neural networks mechanism to estimate feature importance in domain adaptation. For this purpose, we will begin by introducing a novel formulation of the optimization objective. Then, we will develop an adversarial network domain adaptation algorithm adjusting weights given to each feature, ensuring that those related to the target receive higher weights. Finally, we will evaluate our method on public dataset and compare it to other domain adaptation baselines to demonstrate the improvement for regression tasks.