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:

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
JGSA (Joint Geometrical and Statistical Alignment) [6]
- Matlab

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]. 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.