Abstract In this paper, we aim to address the unsupervised domain adaptation problem where the data in the target domain are much more diverse compared with the data in the source domain. In particular, this problem is formulated as discovering and incorporating latent domains underlying target data of interest for unsupervised domain adaptation. More specifically, the discovery of the latent target domains is based on three criteria, including the maximization of compactness and distinctiveness of the data in the individual latent target-domain, as well as the minimization of total divergence from the latent target-domains to the source domain. For each pair formed by a latent target domain and the source domain, we learn a feature space where the discrepancy between the source domain and the specific latent target domain is shrunk. Finally, we consider the projected source domain data on the learned latent feature spaces as different views of the source domain, and propose an extended multiple kernel learning algorithm to train a more robust and precise classifier for predicting the unlabeled target data. The effectiveness of our proposed method is demonstrated on various benchmark datasets for object recognition and human activity recognition. Moreover, we also show that our proposed method can be treated as an effective complement to the deep learning based unsupervised domain adaptation.

中文翻译：

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发现和合并潜在目标域以进行域适应

摘要 在本文中，我们旨在解决无监督域适应问题，即目标域中的数据与源域中的数据相比更加多样化。特别地，这个问题被表述为发现和合并潜在的域，用于无监督的域适应。更具体地说，潜在目标域的发现基于三个标准，包括最大化单个潜在目标域中数据的紧凑性和独特性，以及最小化从潜在目标域到目标域的总差异。源域。对于由潜在目标域和源域形成的每一对，我们学习了一个特征空间，其中源域和特定潜在目标域之间的差异缩小了。最后，我们将学习到的潜在特征空间上的投影源域数据视为源域的不同视图，并提出了一种扩展的多核学习算法来训练更强大和更精确的分类器来预测未标记的目标数据。我们提出的方法的有效性在用于对象识别和人类活动识别的各种基准数据集上得到了证明。此外，我们还表明，我们提出的方法可以被视为对基于深度学习的无监督域适应的有效补充。我们提出的方法的有效性在用于对象识别和人类活动识别的各种基准数据集上得到了证明。此外，我们还表明，我们提出的方法可以被视为对基于深度学习的无监督域适应的有效补充。我们提出的方法的有效性在用于对象识别和人类活动识别的各种基准数据集上得到了证明。此外，我们还表明，我们提出的方法可以被视为对基于深度学习的无监督域适应的有效补充。