Skip to content

Latest commit

 

History

History

code

Folders and files

NameName
Last commit message
Last commit date

parent directory

..
ASR
ASR
 
 
BDA
BDA
 
 
DeepDA
DeepDA
 
 
DeepDG
DeepDG
 
 
clip
clip
 
 
deep
deep
 
 
distance
distance
 
 
feature_extractor
feature_extractor
 
 
traditional
traditional
 
 
utils
utils
 
 
Integrated sensing and communication (LSTM and VGG 16 model for digit and image classification, will be used for sensing and communication in 6G networks)
Integrated sensing and communication (LSTM and VGG 16 model for digit and image classification, will be used for sensing and communication in 6G networks)
 
 
README.md
README.md
 
 

README.md

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.

You are welcome to contribute and suggest other methods.

Codes here are widely used by top conferences and journals:

This document contains codes from several aspects: tutorial, theory, traditional methods, and deep methods.

Testing dataset can be found here.

Notebooks

There's even no need to install a library or package, which will make things worse. I've already put everything you need into a jupyter notebook, which you can access in Google's colab or just see it in this repo here. To run it instantly without any configuration, I also put it to Google's Colab: Colab

Fine-tune

  • Fine-tune using AlexNet and ResNet

  • Safe finetune 安全迁移学习

  • CLIP for zero-shot transfer 使用OpenAI的CLIP模型进行zero-shot迁移

Deep feature extractor 提取深度网络特征用于传统方法

Basic distance 常用的距离度量

Semi-supervised learning

Personalized federated learning

[PersonalizedFL: a unified library for personalized federated learning]

Useful tools 常用工具

  • Feature visualization using t-SNE (用t-SNE进行特征可视化)：Python
  • Gradient Reversal Layer (梯度反转层，GRL)：Pytorch
    • Support all Pytorch versions! (We know that autograd has been changed since 1.0)

Domain generalization 领域泛化

  • DeepDG (Deep domain generalization toolkit) Pytorch

    • Including: ERM, MMD, DANN, CORAL, Mixup, RSC, GroupDRO, etc.

  • DIFEX (Domain-Invariant Feature EXploration, TMLR-22) [88] Pytorch

Traditional transfer learning methods 非深度迁移

  • SVM (baseline) Matlab
  • TCA (Transfer Component Anaysis, TNN-11) [1] Matlab and Python
  • KMM (Kernel Mean Matching, NIPS-06) [67] Python
  • GFK (Geodesic Flow Kernel, CVPR-12) [2] Matlab and Python
  • DA-NBNN (Frustratingly Easy NBNN Domain Adaptation, ICCV-13) [39] Matlab
  • JDA (Joint Distribution Adaptation, ICCV-13) [3] Matlab and Python
  • TJM (Transfer Joint Matching, CVPR-14) [4] Matlab
  • CORAL (CORrelation ALignment, AAAI-15) [5] Matlab and Python | Github
  • JGSA (Joint Geometrical and Statistical Alignment, CVPR-17) [6] Matlab(official) | Matlab(easy)
  • TrAdaBoost (ICML-07)[8] Python
  • SA (Subspace Alignment, ICCV-13) [11] Matlab(official) | Matlab
  • BDA (Balanced Distribution Adaptation for Transfer Learning, ICDM-17) [15] Matlab(official)
  • MTLF (Metric Transfer Learning, TKDE-17) [16] Matlab
  • Open Set Domain Adaptation (ICCV-17) [19] Matlab(official)
  • 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
  • OTL (Online Transfer Learning, ICML-10) [31] Matlab(official)
  • RWA (Random Walking, arXiv, simple but powerful) [46] Matlab
  • MEDA (Manifold Embedded Distribution Alignment, ACM MM-18) [47] Matlab(Official)
  • DeepMEDA (DDAN) (Deep version of MEDA, or DDAN) [82] Pytorch(official)
  • EasyTL (Practically Easy Transfer Learning, ICME-19) [63] Matlab(Official) | Python
  • SCA (Scatter Component Analysis, TPAMI-17) [79] Matlab
  • SOT (Substructural Optimal Transport, arxiv-21) [84] python

Deep transfer learning methods 深度迁移

  • DaNN (Domain Adaptive Neural Network, PRICAI-14) [41] PyTorch
  • DDC (Deep Domain Confusion, arXiv-14) PyTorch
  • DeepCORAL (Deep CORAL: Correlation Alignment for Deep Domain Adaptation) [33] PyTorch(recommend) | PyTorch | 中文解读
  • DAN/JAN (Deep Adaptation Network/Joint Adaptation Network, ICML-15,17) [9,10] PyTorch(Official) | Caffe(Official) | PyTorch(DAN)(recommend)
  • B-JMMD (Balanced Joint Maximum Mean Discrepancy for Deep Transfer Learning, AA-20) Caffe(Official)
  • RTN (Unsupervised Domain Adaptation with Residual Transfer Networks, NIPS-16) [12] Caffe
  • ADDA (Adversarial Discriminative Domain Adaptation, arXiv-17) [13] Tensorflow(Official) | Pytorch | Pytorch(another)
  • DANN/RevGrad (Unsupervised Domain Adaptation by Backpropagation, ICML-15) [14] Caffe(Official) | PyTorch | Pytorch(another) | Tensorflow(third party)
  • DANN Domain-Adversarial Training of Neural Networks (JMLR-16)[17] Python(official) | Tensorflow | PyTorch
  • Associative Domain Adaptation (ICCV-17) [18] Tensorflow
  • Deep Hashing Network for Unsupervised Domain (CVPR-17) [20] Matlab
  • CCSA (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] Pytorch | Tensorflow
  • DRCN (Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation, ECCV-16) [27] Keras | Pytorch
  • Multi-task Autoencoders for Domain Generalization (ICCV-15) [28] Keras
  • Encoder based lifelong learning (ICCV-17) [30] Matlab
  • MECA (Minimal-Entropy Correlation Alignment, ICLR-18) [32] Python
  • WAE (Wasserstein Auto-Encoders, ICLR-18) [34] Python(Tensorflow)
  • ATDA (Asymmetric Tri-training for Unsupervised Domain Adaptation, ICML-15) [35] Pytorch
  • PixelDA_GAN (Unsupervised pixel-level domain adaptation with GAN, CVPR-17) [36] Pytorch
  • ARDA (Adversarial Representation Learning for Domain Adaptation) [37] Pytorch
  • DiscoGAN (Learning to Discover Cross-Domain Relations with Generative Adversarial Networks) [38] Pytorch
  • MCD (Maximum Classifier Discrepancy, CVPR-18) [42] Pytorch(official)
  • Adversarial Feature Augmentation for Unsupervised Domain Adaptation (CVPR-18) [43] Tensorflow
  • DML (Deep Mutual Learning, CVPR-18) [44] Tensorflow
  • Self-ensembling for visual domain adaptation (ICLR 2018) [45] Pytorch
  • iCAN (Incremental Collaborative and Adversarial Network for Unsupervised Domain Adaptation, CVPR-18) [49] Pytorch
  • WeightedGAN (Importance Weighted Adversarial Nets for Partial Domain Adaptation, CVPR-18) [50] Caffe
  • OpenSet (Open Set Domain Adaptation by Backpropagation) [51] Tensorflow
  • WDGRL (Wasserstein Distance Guided Representation Learning, AAAI-18) [52] Pytorch
  • JDDA (Joint Domain Alignment and Discriminative Feature Learning) [53] Tensorflow
  • Multi-modal Cycle-consistent Generalized Zero-Shot Learning (ECCV-18) [54] Tensorflow
  • MSTN (Moving Semantic Transfer Network, ICML-18) [55] Tensorflow | Pytorch
  • SAN (Partial Transfer Learning With Selective Adversarial Networks, CVPR-18) [56] Caffe, Pytorch
  • M-ADDA (Metric-based Adversarial Discriminative Domain Adaptation, ICML-18 workshop) [57] Pytorch
  • Openset_DA (Open Set Domain Adaptation by Backpropagation) [58] Pytorch
  • DIRT-T (A DIRT-T Approach to Unsupervised Domain Adaptation, ICLR-18) [59] Tensorflow
  • CMD (Central Moment Discrepancy, ICLR-17 and InfSc-19) [61], [62] Keras(Theano) | Keras(Theano, journal extension)
  • OPDA_BP (Open Set Domain Adaptation by Back-propagation, ECCV-18) [64] Pytorch(Official)
  • TCP (Transfer Channel Prunning, IJCNN-19) [65] Pytorch(Official)
  • MTAN (Multi-Task Attention Network, CVPR-19) [66] Python
  • L2T_ww (Learning What and Where to Transfer, ICML-19) [68] Pytorch
  • SSDA_MME (Semi-supervised Domain Adaptation via Minimax Entropy, ICCV-19) [71] Pytorch
  • MRAN (Multi-representation adaptation network for cross-domain image classification, Neural Networks 2019) [72] Pytorch
  • TA3N (Temporal Attentive Alignment for Large-Scale Video Domain Adaptation, ICCV-19) [73] Pytorch
  • MDAN (Multiple Source Domain Adaptation with Adversarial Learning, NeurIPS-18) [74] Pytorch
  • Deep model transferribility from attribution maps (NeurIPS-19) [75] Tensorflow
  • DIVA (Domain Invariant Variational Autoencoders, arXiv-19) [76] Pytorch
  • CDCL (Cross-Domain Complementary Learning with Synthetic Data for Multi-Person Part Segmentation, arXiv, ICCV-19 Demo) [77] Tensorflow
  • DTA (Drop to Adapt: Learning Discriminative Features for Unsupervised Domain Adaptation, arXiv, ICCV-19) [78] PyTorch
  • DAAN (Dynamic Adversarial Adaptation Network, ICDM 2019) [80] Pytorch
  • DAEL (Domain Adaptive Ensemble Learning, ArXiv 2020) [81] Pytorch
  • DSAN (Deep Subdomain Adaptation Network for Image Classification, DSAN 2020) [82] Pytorch
  • CSG (Learning Causal Semantic Representation for Out-of-Distribution Prediction) [87] Pytorch

Applications

  • PTUPCDR Personalized Transfer of User Preferences for Cross-domain Recommendation (WSDM-22) [86] Pytorch
  • Learning to select data for transfer learning with Bayesian Optimization (EMNLP-17) [69] Python
  • SDG4DA (Reinforced Training Data Selection for Domain Adaptation, ACL-19) [70] Tensorflow
  • CMatch (Cross-domain Speech Recognition with Unsupervised Character-level Distribution Matching, arXiv-21) [83] Pytorch
  • Adapter for speech recognition (Adapter-based Cross-lingual ASR with EasyEspnet) Pytorch [85]

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.

[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.

[31] Peilin Zhao and Steven C.H. Hoi. OTL: A Framework of Online Transfer Learning. ICML 2010.

[32] Pietro Morerio, Jacopo Cavazza, Vittorio Murino. Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain Adaptation. ICLR 2018.

[33] Sun B, Saenko K. Deep coral: Correlation alignment for deep domain adaptation[C]//European Conference on Computer Vision. Springer, Cham, 2016: 443-450.

[34] Tolstikhin I, Bousquet O, Gelly S, et al. Wasserstein Auto-Encoders[J]. arXiv preprint arXiv:1711.01558, 2017.

[35] Saito K, Ushiku Y, Harada T. Asymmetric tri-training for unsupervised domain adaptation[J]. arXiv preprint arXiv:1702.08400, 2017.

[36] Bousmalis K, Silberman N, Dohan D, et al. Unsupervised pixel-level domain adaptation with generative adversarial networks[C]//The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2017, 1(2): 7.

[37] Shen J, Qu Y, Zhang W, et al. Adversarial representation learning for domain adaptation[J]. arXiv preprint arXiv:1707.01217, 2017.

[38] Kim T, Cha M, Kim H, et al. Learning to discover cross-domain relations with generative adversarial networks[J]. arXiv preprint arXiv:1703.05192, 2017.

[39] Tommasi T, Caputo B. Frustratingly Easy NBNN Domain Adaptation[C]. international conference on computer vision, 2013: 897-904.

[40] Pei Z, Cao Z, Long M, et al. Multi-Adversarial Domain Adaptation[C] // AAAI 2018.

[41] Ghifary M, Kleijn W B, Zhang M. Domain adaptive neural networks for object recognition[C]//Pacific Rim International Conference on Artificial Intelligence. Springer, Cham, 2014: 898-904.

[42] Saito K, Watanabe K, Ushiku Y, et al. Maximum Classifier Discrepancy for Unsupervised Domain Adaptation[J]. arXiv preprint arXiv:1712.02560, 2017.

[43] Volpi R, Morerio P, Savarese S, et al. Adversarial Feature Augmentation for Unsupervised Domain Adaptation[J]. arXiv preprint arXiv:1711.08561, 2017.

[44] Zhang Y, Xiang T, Hospedales T M, et al. Deep Mutual Learning[C]. CVPR 2018.

[45] French G, Mackiewicz M, Fisher M. Self-ensembling for visual domain adaptation[C]//International Conference on Learning Representations. 2018.

[46] van Laarhoven T, Marchiori E. Unsupervised Domain Adaptation with Random Walks on Target Labelings[J]. arXiv preprint arXiv:1706.05335, 2017.

[47] Jindong Wang, Wenjie Feng, Yiqiang Chen, Han Yu, Meiyu Huang, Philip S. Yu. Visual Domain Adaptation with Manifold Embedded Distribution Alignment. ACM Multimedia conference 2018.

[48] Zhangjie Cao, Mingsheng Long, et al. Partial Adversarial Domain Adaptation. ECCV 2018.

[49] Zhang W, Ouyang W, Li W, et al. Collaborative and Adversarial Network for Unsupervised domain adaptation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 3801-3809.

[50] Zhang J, Ding Z, Li W, et al. Importance Weighted Adversarial Nets for Partial Domain Adaptation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 8156-8164.

[51] Saito K, Yamamoto S, Ushiku Y, et al. Open Set Domain Adaptation by Backpropagation[J]. arXiv preprint arXiv:1804.10427, 2018.

[52] Shen J, Qu Y, Zhang W, et al. Wasserstein Distance Guided Representation Learning for Domain Adaptation[C]//AAAI. 2018.

[53] Chen C, Chen Z, Jiang B, et al. Joint Domain Alignment and Discriminative Feature Learning for Unsupervised Deep Domain Adaptation[J]. arXiv preprint arXiv:1808.09347, 2018.

[54] Felix R, Vijay Kumar B G, Reid I, et al. Multi-modal Cycle-consistent Generalized Zero-Shot Learning. ECCV 2018.

[55] Xie S, Zheng Z, Chen L, et al. Learning Semantic Representations for Unsupervised Domain Adaptation[C]//International Conference on Machine Learning. 2018: 5419-5428.

[56] Cao Z, Long M, Wang J, et al. Partial transfer learning with selective adversarial networks. CVPR 2018.

[57] Issam Laradji, Reza Babanezhad. M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning. ICML 2018 workshop.

[58] Saito K, Yamamoto S, Ushiku Y, et al. Open Set Domain Adaptation by Backpropagation[J]. arXiv preprint arXiv:1804.10427, 2018.

[59] Shu R, Bui H H, Narui H, et al. A DIRT-T Approach to Unsupervised Domain Adaptation[J]. arXiv preprint arXiv:1802.08735, 2018.

[60] Mingsheng Long, et al. Conditional Adversarial Domain Adaptation. NeurIPS 2018.

[61] W.Zellinger, T. Grubinger, E. Lughofer, T. Natschlaeger, and Susanne Saminger-Platz, "Central moment discrepancy (cmd) for domain-invariant representation learning," ICLR 2017.

[62] W. Zellinger, B.A. Moser, T. Grubinger, E. Lughofer, T. Natschlaeger, and S. Saminger-Platz, "Robust unsupervised domain adaptation for neural networks via moment alignment," Information Sciences (in press), 2019, https://doi.org/10.1016/j.ins.2019.01.025, arXiv preprint arxiv:1711.06114

[63] Jindong Wang, Yiqiang Chen, Han Yu, Meiyu Huang, Qiang Yang. Easy Transfer Learning By Exploiting Intra-domain Structures. IEEE International Conference on Multimedia & Expo (ICME) 2019.

[64] Saito K, Yamamoto S, Ushiku Y, et al. Open set domain adaptation by backpropagation[C]//Proceedings of the European Conference on Computer Vision (ECCV). 2018: 153-168.

[65] Chaohui Yu, Jindong Wang, Yiqiang Chen, Zijing Wu. Accelerating Deep Unsupervised Domain Adaptation with Transfer Channel Pruning. IJCNN 2019.

[66] Shikun Liu, Edward Johns, and Andrew Davison. End-to-End Multi-Task Learning with Attention. CVPR 2019.

[67] Huang J, Gretton A, Borgwardt K, et al. Correcting sample selection bias by unlabeled data[C]//Advances in neural information processing systems. 2007: 601-608.

[68] Yunhun Jang, Hankook Lee, Sung Ju Hwang, Jinwoo Shin. Learning what and where to transfer. ICML 2019.

[69] Sebastian Ruder, Barbara Plank (2017). Learning to select data for transfer learning with Bayesian Optimization. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark.

[70] Liu M, Song Y, Zou H, et al. Reinforced Training Data Selection for Domain Adaptation[C]//Proceedings of the 57th Conference of the Association for Computational Linguistics. 2019: 1957-1968.

[71] Saito K, Kim D, Sclaroff S, et al. Semi-supervised Domain Adaptation via Minimax Entropy. ICCV 2019.

[72] Zhu Y, Zhuang F, Wang J, et al. Multi-representation adaptation network for cross-domain image classification[J]. Neural Networks, 2019.

[73] Min-Hung Chen, Zsolt Kira, Ghassan AlRegib, et al. Temporal Attentive Alignment for Large-Scale Video Domain Adaptation. ICCV 2019.

[74] Zhao H, Zhang S, Wu G, et al. Multiple source domain adaptation with adversarial learning. NeurIPS 2018.

[75] Jie Song, et al. Deep model transferrability from attirbution maps. NeurIPS 2019.

[76] Ilse, M., Tomczak, J. M., C. Louizos & Welling, M. (2018). DIVA: Domain Invariant Variational Autoencoders. arXiv preprint arXiv:1905.10427

[77] Lin K., et al. Cross-Domain Complementary Learning with Synthetic Data for Multi-Person Part Segmentation[J]. arXiv preprint arXiv:1907.05193, ICCV demo, 2019.

[78] Lee S., Kim D., et al. Drop to Adapt: Learning Discriminative Features for Unsupervised Domain Adaptation. ICCV 2019.

[79] Ghifary M, Balduzzi D, Kleijn W B, et al. Scatter component analysis: A unified framework for domain adaptation and domain generalization[J]. IEEE transactions on pattern analysis and machine intelligence, 2016, 39(7): 1414-1430.

[80] Chaohui Yu, Jindong Wang, Yiqiang Chen, Meihu Huang. Transfer learnign with dynamic adversarial adaptation network. ICDM 2019.

[81] Kaiyang Zhou, Yongxin Yang, Yu Qiao, Tao Xiang. Domain Adaptive Ensemble Learning. ArXiv preprint, 2020.

[82] Wang J, Chen Y, Feng W, et al. Transfer learning with dynamic distribution adaptation[J]. ACM Transactions on Intelligent Systems and Technology (TIST), 2020, 11(1): 1-25.

[83] Wenxin Hou, Jindong Wang, Xu Tan, Tao Qin, Takahiro Shinozaki, "Cross-domain Speech Recognition with Unsupervised Character-level Distribution Matching", arxiv 2104.07491.

[84] Lu W, Chen Y, Wang J, et al. Cross-domain Activity Recognition via Substructural Optimal Transport[J]. arXiv preprint arXiv:2102.03353, 2021.

[85] Hou W, Zhu H, Wang Y, et al. Exploiting Adapters for Cross-lingual Low-resource Speech Recognition[J]. arXiv preprint arXiv:2105.11905, 2021.

[86] Yongchun Zhu, ZhenWei Tang, Yudan Liu, et al. Personalized Transfer of User Preferences for Cross-domain Recommendation[C]. WSDM, 2022.

[87] Chang Liu, Xinwei Sun, Jindong Wang, et al. Learning Causal Semantic Representation for Out-of-Distribution Prediction. NeurIPS 2021.

[88] Lu et al. Domain-invariant feature exploration for domain generalization. TMLR 2022.