Collaborative representation with curriculum classifier boosting for unsupervised domain adaptation

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.