Constrained Deep Transfer Feature Learning and Its Applications

@article{Wu2016ConstrainedDT,
  title={Constrained Deep Transfer Feature Learning and Its Applications},
  author={Y. Wu and Q. Ji},
  journal={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2016},
  pages={5101-5109}
}
  • Y. Wu, Q. Ji
  • Published 2016
  • Computer Science
  • 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • Feature learning with deep models has achieved impressive results for both data representation and classification for various vision tasks. Deep feature learning, however, typically requires a large amount of training data, which may not be feasible for some application domains. Transfer learning can be one of the approaches to alleviate this problem by transferring data from data-rich source domain to data-scarce target domain. Existing transfer learning methods typically perform one-shot… CONTINUE READING

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