This work focuses on unsupervised visual domain adaptation which is still challenging in visual recognition. Most of the attention has been dedicated to seeking the domain-invariant features of cross-domain data, but they ignores the valuable discriminative information in the source domain. In this paper, we propose a Discriminative Dictionary Evolution (DDE) approach to seek discriminative features robust to domain shift. Specifically, DDE gradually adapts a discriminative dictionary learned from the source domain to the target domain through a dictionary evolving procedure, in which self-selected atoms of the dictionary are updated with \(\ell _{2,1}\)-norm-based regularization. DDE produces domain-invariant representations for cross-domain visual recognition meanwhile promotes the discriminativeness of the dictionary. Empirical results on real-world data sets demonstrate the advantages of the proposed approach over existing competitive methods.

中文翻译：

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通过有区别的词典演化进行无监督的视觉域适应

这项工作侧重于在视觉识别方面仍具有挑战性的无监督视觉域适应。大部分注意力都集中在寻找跨域数据的域不变特征上，但它们忽略了源域中有价值的区分信息。在本文中，我们提出了一种区分词典演化（DDE）方法，以寻求对域移位具有鲁棒性的区分特征。具体来说，DDE通过词典演化过程逐步将从源域中学习的判别词典改编为目标域，在词典中，用\（\ ell _ {2,1} \）更新词典的自选原子-基于规范的正则化。DDE产生用于跨域视觉识别的领域不变表示，同时促进了字典的区分性。实际数据集上的经验结果证明了该方法相对于现有竞争方法的优势。