Semi-Supervised Representation Learning: Transfer Learning with Manifold Regularized Auto-Encoders

Abstract

The excellent performance of transfer learning has emerged in the past few years. How to find feature representations which minimizes the distance between source and target domain is the crucial problem in transfer learning. Recently, deep learning methods have been proposed to learn higher level and robust representation. However, in traditional methods, label information in source domain is not designed to optimize both feature representations and parameters of the learning model. Additionally, data redundance may incur performance degradation on transfer learning. To address these problems, we propose a novel semi-supervised representation learning framework for transfer learning. To obtain this framework, manifold regularization is integrated for the parameters optimization, and the label information is encoded using a softmax regression model in auto-encoders. Meanwhile, whitening layer is introduced to reduce data redundance before auto-encoders. Extensive experiments demonstrate the effectiveness of our proposed framework compared to other competing state-of-the-art baseline methods.