Deep Transfer Learning via Minimum Enclosing Balls

  • Zhilong Deng
  • Fan Liu
  • Jiangjiang Zhao
  • Qiang Wei
  • Shaoning Pang
  • Yue Leng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11303)


Training of deep learning algorithms such as CNN, LSTM, or GRU often requires large amount of data. However in real world applications, the amount of data especially labelled data is limited. To address this challenge, we study Deep Transfer Learning (DTL) in the context of Multitasking Learning (MTL) to extract sharable knowledge from tasks and use it for related tasks. In this paper, we use Minimum Closed Ball (MEB) as a flexible knowledge representation method to map shared domain knowledge from primary task to secondary task in multitasking learning. The experiments provide both analytic and empirical results to show the effectiveness and robustness of the proposed MEB-based deep transfer learning.


Multi-task learning Deep transfer learning Learner-independent multi-task learning Minimum enclosing ball 


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Zhilong Deng
    • 1
  • Fan Liu
    • 2
  • Jiangjiang Zhao
    • 1
  • Qiang Wei
    • 1
  • Shaoning Pang
    • 3
  • Yue Leng
    • 4
  1. 1.Algorithm Group, Research and Development DepartmentOnline Services of China MobileBeijingChina
  2. 2.Artificial Intelligence LabMeituan-Dianping GroupBeijingChina
  3. 3.Department of ComputingUnitec Institute of TechnologyAucklandNew Zealand
  4. 4.Algorithm Group, Artificial Intelligence LabEmotibot Technologies LimitedBeijingChina

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