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code_transfer_learning

Some useful transfer learning and domain adaptation codes

It is a waste of time looking for the codes from others. So I collect or reimplement them here in a way that you can easily use. The following are some of the popular transfer learning (domain adaptation) methods in recent years, and I know most of them will be chosen to compare with your own method.

It is still on the go. You are welcome to contribute and suggest other methods.


Availiable codes for:

Non-deep learning

Deep learning

  • DAN/JAN (Deep Adaptation Network/Joint Adaptation Network, ICML-15,17) [9,10]
  • RTN (Unsupervised Domain Adaptation with Residual Transfer Networks, NIPS-16) [12]
  • ADDA (Adversarial Discriminative Domain Adaptation, arXiv-17) [13]
  • Unsupervised Domain Adaptation by Backpropagation (ICML-15) [14]
  • Domain-Adversarial Training of Neural Networks (JMLR-16)[17]
  • Associative Domain Adaptation (ICCV-17) [18]
  • Deep Hashing Network for Unsupervised Domain (CVPR-17) [20]

Code from HKUST [a bit old]


Testing dataset can be found here.


References

[1] Pan S J, Tsang I W, Kwok J T, et al. Domain adaptation via transfer component analysis[J]TNN, 2011, 22(2): 199-210.

[2] Gong B, Shi Y, Sha F, et al. Geodesic flow kernel for unsupervised domain adaptation[C]//CVPR, 2012: 2066-2073.

[3] Long M, Wang J, Ding G, et al. Transfer feature learning with joint distribution adaptation[C]//ICCV. 2013: 2200-2207.

[4] Long M, Wang J, Ding G, et al. Transfer joint matching for unsupervised domain adaptation[C]//CVPR. 2014: 1410-1417.

[5] Sun B, Feng J, Saenko K. Return of Frustratingly Easy Domain Adaptation[C]//AAAI. 2016, 6(7): 8.

[6] Zhang J, Li W, Ogunbona P. Joint Geometrical and Statistical Alignment for Visual Domain Adaptation[C]//CVPR 2017.

[7] Long M, Wang J, Ding G, et al. Adaptation regularization: A general framework for transfer learning[J]//TKDE, 2014, 26(5): 1076-1089.

[8] Dai W, Yang Q, Xue G R, et al. Boosting for transfer learning[C]//ICML, 2007: 193-200.

[9] Long M, Cao Y, Wang J, et al. Learning transferable features with deep adaptation networks[C]//ICML. 2015: 97-105.

[10] Long M, Wang J, Jordan M I. Deep transfer learning with joint adaptation networks[J]//ICML 2017.

[11] Fernando B, Habrard A, Sebban M, et al. Unsupervised visual domain adaptation using subspace alignment[C]//ICCV. 2013: 2960-2967.

[12] Long M, Zhu H, Wang J, et al. Unsupervised domain adaptation with residual transfer networks[C]//NIPS. 2016.

[13] Tzeng E, Hoffman J, Saenko K, et al. Adversarial discriminative domain adaptation[J]. arXiv preprint arXiv:1702.05464, 2017.

[14] Ganin Y, Lempitsky V. Unsupervised domain adaptation by backpropagation[C]//International Conference on Machine Learning. 2015: 1180-1189.

[15] Jindong Wang, Yiqiang Chen, Shuji Hao, Wenjie Feng, and Zhiqi Shen. Balanced Distribution Adaptation for Transfer Learning. ICDM 2017.

[16] Y. Xu et al., "A Unified Framework for Metric Transfer Learning," in IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 6, pp. 1158-1171, June 1 2017. doi: 10.1109/TKDE.2017.2669193

[17] Ganin Y, Ustinova E, Ajakan H, et al. Domain-adversarial training of neural networks[J]. Journal of Machine Learning Research, 2016, 17(59): 1-35.

[18] Haeusser P, Frerix T, Mordvintsev A, et al. Associative Domain Adaptation[C]. ICCV, 2017.

[19] Pau Panareda Busto, Juergen Gall. Open set domain adaptation. ICCV 2017.

[20] Venkateswara H, Eusebio J, Chakraborty S, et al. Deep hashing network for unsupervised domain adaptation[C]. CVPR 2017.

[21] H. Lu, L. Zhang, et al. When Unsupervised Domain Adaptation Meets Tensor Representations. ICCV 2017.