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code_transfer_learning

Transfer learning toolbox; some useful transfer learning and domain adaptation codes

It is a waste of time looking for the codes from others. So I clean and reimplement them here in a way that you can easily understand and 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

  • TCA (Transfer Component Anaysis) [1]
  • GFK (Geodesic Flow Kernel) [2]
  • JDA (Joint Distribution Adaptation) [3]
  • TJM (Transfer Joint Matching) [4]
  • CORAL (CORrelation ALignment) [5]
  • JGSA (Joint Geometrical and Statistical Alignment) [6]
  • ARTL (Adaptation Regularization) [7]
  • TrAdaBoost [8]
  • SA (Subspace Alignment) [11]
  • BDA (Balanced Distribution Adaptation for Transfer Learning) [15]

Deep learning

  • DAN/JAN (Deep Adaptation Network/Joint Adaptation Network) [9,10]
  • RTN (Unsupervised Domain Adaptation with Residual Transfer Networks) [12]
  • ADDA (Adversarial Discriminative Domain Adaptation) [13]
  • Unsupervised Domain Adaptation by Backpropagation [14]

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.