Domain adaptation aims at leveraging rich knowledge in the source domain to build an accurate classifier in the different but related target domain. Most prior methods attempt to align features or reduce domain discrepancy by means of statistical properties yet ignore the differences among samples. In this paper, we put forward a novel solution based on collaborative representation for classifier adaptation. Similar to instance re-weighting, we aim to learn an adaptive classifier by multi-stage inference and instance rearranging. Specifically, a curriculum learning based sample selection scheme is proposed, then the chosen samples are integrated into training set iteratively. Due to the distribution mismatch of two domains, we propose distance-aware sparsity regularization to learn more flexible representations. Extensive experiments verify that the proposed method is comparable or superior to the state-of-the-art methods.

领域适应旨在利用源领域中的丰富知识，在不同但相关的目标领域中建立准确的分类器。大多数现有方法尝试通过统计属性来对齐特征或减少域差异，却忽略了样本之间的差异。在本文中，我们提出了一种基于协作表示的分类器自适应解决方案。与实例重新加权类似，我们旨在通过多阶段推理和实例重排来学习自适应分类器。具体而言，提出了一种基于课程学习的样本选择方案，然后将选择的样本迭代地集成到训练集中。由于两个域的分布不匹配，我们提出了距离感知的稀疏性正则化方法，以学习更灵活的表示形式。