A Survey on Deep Transfer Learning

  • Chuanqi TanEmail author
  • Fuchun Sun
  • Tao Kong
  • Wenchang Zhang
  • Chao Yang
  • Chunfang Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11141)


As a new classification platform, deep learning has recently received increasing attention from researchers and has been successfully applied to many domains. In some domains, like bioinformatics and robotics, it is very difficult to construct a large-scale well-annotated dataset due to the expense of data acquisition and costly annotation, which limits its development. Transfer learning relaxes the hypothesis that the training data must be independent and identically distributed (i.i.d.) with the test data, which motivates us to use transfer learning to solve the problem of insufficient training data. This survey focuses on reviewing the current researches of transfer learning by using deep neural network and its applications. We defined deep transfer learning, category and review the recent research works based on the techniques used in deep transfer learning.


Deep transfer learning Transfer learning Survey 


  1. 1.
    Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M.: Domain-adversarial neural networks. arXiv preprint arXiv:1412.4446 (2014)
  2. 2.
    Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein GAN. arXiv preprint arXiv:1701.07875 (2017)
  3. 3.
    Chang, H., Han, J., Zhong, C., Snijders, A., Mao, J.H.: Unsupervised transfer learning via multi-scale convolutional sparse coding for biomedical applications. IEEE Trans. Patt. Anal. Mach. Intell. 40(5), 1182–1194 (2017)CrossRefGoogle Scholar
  4. 4.
    Dai, W., Yang, Q., Xue, G.R., Yu, Y.: Boosting for transfer learning. In: Proceedings of the 24th International Conference on Machine Learning, pp. 193–200. ACM (2007)Google Scholar
  5. 5.
    Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. arXiv preprint arXiv:1409.7495 (2014)
  6. 6.
    George, D., Shen, H., Huerta, E.: Deep transfer learning: a new deep learning glitch classification method for advanced LIGO. arXiv preprint arXiv:1706.07446 (2017)
  7. 7.
    Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)Google Scholar
  8. 8.
    Gretton, A., et al.: Optimal kernel choice for large-scale two-sample tests. In: Advances in Neural Information Processing Systems, pp. 1205–1213 (2012)Google Scholar
  9. 9.
    Huang, J.T., Li, J., Yu, D., Deng, L., Gong, Y.: Cross-language knowledge transfer using multilingual deep neural network with shared hidden layers. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 7304–7308. IEEE (2013)Google Scholar
  10. 10.
    Li, N., Hao, H., Gu, Q., Wang, D., Hu, X.: A transfer learning method for automatic identification of sandstone microscopic images. Comput. Geosci. 103, 111–121 (2017)CrossRefGoogle Scholar
  11. 11.
    Liu, X., Liu, Z., Wang, G., Cai, Z., Zhang, H.: Ensemble transfer learning algorithm. IEEE Access 6, 2389–2396 (2018)CrossRefGoogle Scholar
  12. 12.
    Long, M., Cao, Y., Wang, J., Jordan, M.: Learning transferable features with deep adaptation networks. In: International Conference on Machine Learning, pp. 97–105 (2015)Google Scholar
  13. 13.
    Long, M., Cao, Z., Wang, J., Jordan, M.I.: Domain adaptation with randomized multilinear adversarial networks. arXiv preprint arXiv:1705.10667 (2017)
  14. 14.
    Long, M., Wang, J., Jordan, M.I.: Deep transfer learning with joint adaptation networks. arXiv preprint arXiv:1605.06636 (2016)
  15. 15.
    Long, M., Zhu, H., Wang, J., Jordan, M.I.: Unsupervised domain adaptation with residual transfer networks. In: Advances in Neural Information Processing Systems, pp. 136–144 (2016)Google Scholar
  16. 16.
    Luo, Z., Zou, Y., Hoffman, J., Fei-Fei, L.F.: Label efficient learning of transferable representations acrosss domains and tasks. In: Advances in Neural Information Processing Systems, pp. 164–176 (2017)Google Scholar
  17. 17.
    Oquab, M., Bottou, L., Laptev, I., Sivic, J.: Learning and transferring mid-level image representations using convolutional neural networks. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1717–1724. IEEE (2014)Google Scholar
  18. 18.
    Pan, S.J., Tsang, I.W., Kwok, J.T., Yang, Q.: Domain adaptation via transfer component analysis. IEEE Trans. Neural Netw. 22(2), 199–210 (2011)CrossRefGoogle Scholar
  19. 19.
    Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)CrossRefGoogle Scholar
  20. 20.
    Pardoe, D., Stone, P.: Boosting for regression transfer. In: Proceedings of the 27th International Conference on International Conference on Machine Learning, pp. 863–870. Omnipress (2010)Google Scholar
  21. 21.
    Tzeng, E., Hoffman, J., Darrell, T., Saenko, K.: Simultaneous deep transfer across domains and tasks. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 4068–4076. IEEE (2015)Google Scholar
  22. 22.
    Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. In: Computer Vision and Pattern Recognition (CVPR), vol. 1, p. 4 (2017)Google Scholar
  23. 23.
    Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., Darrell, T.: Deep domain confusion: maximizing for domain invariance. arXiv preprint arXiv:1412.3474 (2014)
  24. 24.
    Wan, C., Pan, R., Li, J.: Bi-weighting domain adaptation for cross-language text classification. In: IJCAI Proceedings of International Joint Conference on Artificial Intelligence, vol. 22, p. 1535 (2011)Google Scholar
  25. 25.
    Weiss, K., Khoshgoftaar, T.M., Wang, D.: A survey of transfer learning. J. Big Data 3(1), 9 (2016)CrossRefGoogle Scholar
  26. 26.
    Xu, Y., et al.: A unified framework for metric transfer learning. IEEE Trans. Knowl. Data Eng. 29(6), 1158–1171 (2017)CrossRefGoogle Scholar
  27. 27.
    Yao, Y., Doretto, G.: Boosting for transfer learning with multiple sources. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1855–1862. IEEE (2010)Google Scholar
  28. 28.
    Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: Advances in Neural Information Processing Systems, pp. 3320–3328 (2014)Google Scholar
  29. 29.
    Zhang, J., Li, W., Ogunbona, P.: Joint geometrical and statistical alignment for visual domain adaptation. In: CVPR (2017)Google Scholar
  30. 30.
    Zhu, H., Long, M., Wang, J., Cao, Y.: Deep hashing network for efficient similarity retrieval. In: AAAI, pp. 2415–2421 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Chuanqi Tan
    • 1
    Email author
  • Fuchun Sun
    • 1
  • Tao Kong
    • 1
  • Wenchang Zhang
    • 1
  • Chao Yang
    • 1
  • Chunfang Liu
    • 1
  1. 1.State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Computer Science and TechnologyTsinghua UniversityBeijingChina

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