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.

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Availiable codes for:

Non-deep learning

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

Deep learning

• DAN/JAN (Deep Adaptation Network/Joint Adaptation Network) [9,10]
  • Caffe

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Code from HKUST [a bit old]

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Testing dataset can be found here.

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References

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

[2] Gong B, Shi Y, Sha F, et al. Geodesic flow kernel for unsupervised domain adaptation[C]//Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on. IEEE, 2012: 2066-2073.

[3] Long M, Wang J, Ding G, et al. Transfer feature learning with joint distribution adaptation[C]//Proceedings of the IEEE international conference on computer vision. 2013: 2200-2207.

[4] Long M, Wang J, Ding G, et al. Transfer joint matching for unsupervised domain adaptation[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 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]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(5): 1076-1089.

[8] Dai W, Yang Q, Xue G R, et al. Boosting for transfer learning[C]//Proceedings of the 24th international conference on Machine learning. ACM, 2007: 193-200.

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

[10] Long M, Wang J, Jordan M I. Deep transfer learning with joint adaptation networks[J]. arXiv preprint arXiv:1605.06636, 2016.

[11] Fernando B, Habrard A, Sebban M, et al. Unsupervised visual domain adaptation using subspace alignment[C]//Proceedings of the IEEE international conference on computer vision. 2013: 2960-2967.