Unsupervised domain adaptation leverages rich information from a labeled source domain to model an unlabeled target domain. Existing methods attempt to align the cross-domain distributions. However, the statistical representations of the alignment of the two domains are not well addressed. In this paper, we propose deep least squares alignment (DLSA) to estimate the distribution of the two domains in a latent space by parameterizing a linear model. We further develop marginal and conditional adaptation loss to reduce the domain discrepancy by minimizing the angle between fitting lines and intercept differences and further learning domain invariant features. Extensive experiments demonstrate that the proposed DLSA model is effective in aligning domain distributions and outperforms state-of-the-art methods.

翻译：未受监督的域适应利用标签源域的丰富信息来模拟无标签目标域。现有方法试图调整跨域分布。但是,这两个域对齐的统计表示没有很好地处理。在本文中,我们提议通过线性模型参数化来估计隐蔽空间内两个域的分布,以进行深度最小方对齐。我们进一步开发边际和有条件的适应损失,以通过尽量减少安装线与截取差异之间的角和进一步学习变量域之间的角来缩小域差异。广泛的实验表明,拟议的DLSA模型在协调域分布和超越最先进的方法方面是有效的。