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Transfer learning with deep manifold regularized auto-encoders
انتقال یادگیری با رمزگذار خودکار تنظیم شده در منیفولد عمیق-2019 The excellent performance of transfer learning has emerged in the past few years. How to find feature representations which minimize the distance between source and target domains is a crucial problem in transfer learning. Recently, deep learning methods have been proposed to learn higher level and ro- bust representations. However, in traditional methods, label information in source domain is not designed to optimize both feature representations and parameters of the learning model. Additionally, the redun- dancy of data may incur performance degradation on transfer learning. To address these problems, we propose a novel semi-supervised representation deep learning framework for transfer learning. To obtain this framework, manifold regularization is integrated for the parameter optimization, and the label infor- mation is encoded using a softmax regression model in auto-encoders. Meanwhile, whitening layer is in- troduced to reduce the redundancy of data before auto-encoders. Extensive experiments demonstrate the effectiveness of our proposed framework compared to other competing state-of-the-art baseline methods Keywords: Transfer learning | Manifold regularization | Stacked denoising auto-encoder |
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