Robust transfer learning based on Geometric Mean Metric Learning

Abstract

Transfer learning usually utilizes the knowledge learned from the relative labeled source domain to promote the model performance in the unlabeled or few labeled target domain with different distribution. Most of the existing transfer learning methods aim to reduce the discrepancy of distributions between the source and target domains, but ignore the discriminative category information involved in the data from both domains in the process of knowledge transfer. To learn more discriminative feature representation in knowledge transfer, this paper integrates the transfer learning and metric learning into a unified framework and proposes a novel robust transfer learning based on geometric mean metric learning, namely Geometric Mean Transfer Learning (GMTL). GMTL uses weighted geometric mean metric learning to model the intra-class distance and the inter-class similarity. In the meantime, the marginal distributions and conditional distributions of the source and target domains are jointly adapted. Moreover, according to the natures of the datasets in different tasks, we dynamically combine the discriminative modeling and domain adaption to make the proposed model more robust. We assign different weights to the intra-class distance and the inter-class similarity in metric learning and different weights to marginal distribution adaption and conditional distribution adaption, respectively. Finally, the solution to the objective function is converted to the problem of finding a point on the geodesic joining two points on the Riemannian manifold, which is very simple and direct. Extensive experiments are conducted on six datasets widely adopted in transfer learning to verify the superiority of our proposed GMTL over existing state-of-the-art transfer learning methods.