Joint subspace and discriminative learning for self-paced domain adaptation

Unsupervised domain adaptation aims to address the problem in which the source data and target data are related but distributed differently. A widely-used two-stage strategy is to learn a domain-invariant subspace, and then train a cross-domain classifier on the resulting subspace. In this paper, we propose a single-stage domain adaption approach for joint subspace learning and discriminative learning. Specifically, a domain-invariant subspace and a cross-domain classifier are progressively learnt in a self-paced learning fashion. To avoid unlabeled target data dominating the overall loss and misleading model training, we progressively include more target data from “easy” to “complex” to optimize our model. Specifically, we propose an alternative optimization algorithm to efficiently find a reasonable solution for our task. Extensive experiments are conducted on multiple standard benchmarks to verify the effectiveness of the proposed approach. The results demonstrate that our model can outperform state-of-the-art non-deep domain adaptation methods.