Discriminative Factor Alignment across Heterogeneous Feature Space

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• 作者：Fangwei Hu (1) hufangwei@apex.sjtu.edu.cn
  Tianqi Chen (1) tqchen@apex.sjtu.edu.cn
  Nathan N. Liu (2) nliu@cse.ust.hk
  Qiang Yang (2) qyang@cse.ust.hk
  Yong Yu (1) yyu@apex.sjtu.edu.cn
• 刊名：Lecture Notes in Computer Science
• 出版年：2012
• 出版时间：2012
• 年：2012
• 卷：7524
• 期：1
• 页码：757-772
• 全文大小：1.0 MB
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• 作者单位：1. Shanghai Jiao Tong University, Shanghai, China2. Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
• ISSN：1611-3349

文摘

Transfer learning as a new machine learning paradigm has gained increasing attention lately. In situations where the training data in a target domain are not sufficient to learn predictive models effectively, transfer learning leverages auxiliary source data from related domains for learning. While most of the existing works in this area are only focused on using the source data with the same representational structure as the target data, in this paper, we push this boundary further by extending transfer between text and images.