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.

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Availiable codes for:

Non-deep learning

• TCA (Transfer Component Anaysis, TNN-11) [1]
  • Matlab(Recommended!) | Python
• GFK (Geodesic Flow Kernel, CVPR-12) [2]
  • Matlab
• JDA (Joint Distribution Adaptation, ICCV-13) [3]
  • Matlab
• TJM (Transfer Joint Matching, CVPR-14) [4]
  • Matlab
• CORAL (CORrelation ALignment, AAAI-15) [5]
  • Matlab | Github
• JGSA (Joint Geometrical and Statistical Alignment, CVPR-17) [6]
  • Matlab
• ARTL (Adaptation Regularization, TKDE-14) [7]
  • Matlab
• TrAdaBoost (ICML-07)[8]
  • Python
• SA (Subspace Alignment, ICCV-13) [11]
  • Matlab
• BDA (Balanced Distribution Adaptation for Transfer Learning, ICDM-17) [15]
  • Matlab
• MTLF (Metric Transfer Learning, TKDE-17) [16]
  • Matlab
• Open Set Domain Adaptation (ICCV-17) [19]
  • Matlab(official, but not available now) | Matlab(My implementation)
• TAISL (When Unsupervised Domain Adaptation Meets Tensor Representations, ICCV-17) [21]
  • Matlab(official)
• STL (Stratified Transfer Learning for Cross-domain Activity Recognition, PerCom-18) [22]
  • Matlab
• LSA (Landmarks-based kernelized subspace alignment for unsupervised domain adaptation, CVPR-15) [29]
  • Matlab

Deep learning

• DAN/JAN (Deep Adaptation Network/Joint Adaptation Network, ICML-15,17) [9,10]
  • Caffe
• RTN (Unsupervised Domain Adaptation with Residual Transfer Networks, NIPS-16) [12]
  • Caffe
• ADDA (Adversarial Discriminative Domain Adaptation, arXiv-17) [13]
  • Python(Tensorflow)
• Unsupervised Domain Adaptation by Backpropagation (ICML-15) [14]
  • Caffe(from author)|Tensorflow(third party)
• Domain-Adversarial Training of Neural Networks (JMLR-16)[17]
  • Tensorflow
• Associative Domain Adaptation (ICCV-17) [18]
  • Tensorflow
• Deep Hashing Network for Unsupervised Domain (CVPR-17) [20]
  • Matlab
• CCSL (Unified Deep Supervised Domain Adaptation and Generalization, ICCV-17) [23]
  • Python(Keras)
• MRN (Learning Multiple Tasks with Multilinear Relationship Networks, NIPS-17) [24]
  • Pytorch
• AutoDIAL (Automatic DomaIn Alignment Layers, ICCV-17) [25]
  • Caffe
• DSN (Domain Separation Networks, NIPS-16) [26]
  • Tensorflow
• DRCN (Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation, ECCV-16) [27]
  • Keras
• Multi-task Autoencoders for Domain Generalization (ICCV-15) [28]
  • Keras
• Encoder based lifelong learning (ICCV-17) [30]
  • Matlab

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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]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.

[22] J. Wang, Y. Chen, L. Hu, X. Peng, and P. Yu. Stratified Transfer Learning for Cross-domain Activity Recognition. 2018 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[23] Motiian S, Piccirilli M, Adjeroh D A, et al. Unified deep supervised domain adaptation and generalization[C]//The IEEE International Conference on Computer Vision (ICCV). 2017, 2.

[24] Long M, Cao Z, Wang J, et al. Learning Multiple Tasks with Multilinear Relationship Networks[C]//Advances in Neural Information Processing Systems. 2017: 1593-1602.

[25] Maria Carlucci F, Porzi L, Caputo B, et al. AutoDIAL: Automatic DomaIn Alignment Layers[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017: 5067-5075.

[26] Bousmalis K, Trigeorgis G, Silberman N, et al. Domain separation networks[C]//Advances in Neural Information Processing Systems. 2016: 343-351.

[27] M. Ghifary, W. B. Kleijn, M. Zhang, D. Balduzzi, and W. Li. "Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation (DRCN)", European Conference on Computer Vision (ECCV), 2016

[28] M. Ghifary, W. B. Kleijn, M. Zhang, D. Balduzzi. Domain Generalization for Object Recognition with Multi-task Autoencoders, accepted in International Conference on Computer Vision (ICCV 2015), Santiago, Chile.

[29] Aljundi R, Emonet R, Muselet D, et al. Landmarks-based kernelized subspace alignment for unsupervised domain adaptation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015: 56-63.

[30] Rannen A, Aljundi R, Blaschko M B, et al. Encoder based lifelong learning[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017: 1320-1328.