迁移学习——Domain Adaptation文献20202019201820172016201520142013

78 阅读 0 评论 52 点赞

我是靠谱客的博主魔幻宝贝，最近开发中收集的这篇文章主要介绍迁移学习——Domain Adaptation文献20202019201820172016201520142013，觉得挺不错的，现在分享给大家，希望可以做个参考。

概述

人工智能文献记录专栏，专栏地址：https://blog.csdn.net/u014157632/category_9760481.html，总目录：https://blog.csdn.net/u014157632/article/details/104578738。不定期更新

2020

Gradients as Features for Deep Representation Learning
Bridging Theory and Algorithm for Domain Adaptation

2019

Yu C, Wang J, Chen Y, et al. Accelerating deep unsupervised domain adaptation with transfer channel pruning[C]//2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019: 1-8.
Wang Z, Dai Z, Póczos B, et al. Characterizing and avoiding negative transfer [C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019: 11293-11302.
Bouvier V, Very P, Hudelot C, et al. Hidden Covariate Shift: A Minimal Assumption For Domain Adaptation[J]. arXiv preprint arXiv:1907.12299, 2019.
Cai G, Wang Y, He L, et al. Unsupervised domain adaptation with adversarial residual transform networks[J]. IEEE Transactions on Neural Networks and Learning Systems, 2019.
- [ ] Zhai X , Puigcerver J , Kolesnikov A , et al. The Visual Task Adaptation Benchmark[J]. 2019.
Guo Y , Li Y , Wang L , et al. AdaFilter: Adaptive Filter Fine-tuning for Deep Transfer Learning[J]. 2019.

2018

Li Y, Wang N, Shi J, et al. Adaptive batch normalization for practical domain adaptation[J]. Pattern Recognition, 2018, 80: 109-117.
Rozantsev A, Salzmann M, Fua P. Beyond sharing weights for deep domain adaptation[J]. IEEE transactions on pattern analysis and machine intelligence, 2018, 41(4): 801-814.
Bhushan Damodaran B, Kellenberger B, Flamary R, et al. Deepjdot: Deep joint distribution optimal transport for unsupervised domain adaptation[C]//Proceedings of the European Conference on Computer Vision (ECCV). 2018: 447-463.
Dong N, Xing E P. Domain adaption in one-shot learning[C]//Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, Cham, 2018: 573-588.
Gautheron L, Redko I, Lartizien C. Feature selection for unsupervised domain adaptation using optimal transport[C]//Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, Cham, 2018: 759-776.
Chadha A, Andreopoulos Y. Improving adversarial discriminative domain adaptation[J]. arXiv preprint arXiv:1809.03625, 2018.
Cui Y, Song Y, Sun C, et al. Large scale fine-grained categorization and domain-specific transfer learning[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 4109-4118.
Yang B, Ma A J, Yuen P C. Learning domain-shared group-sparse representation for unsupervised domain adaptation[J]. Pattern Recognition, 2018, 81: 615-632.
Ying W, Zhang Y, Huang J, et al. Transfer learning via learning to transfer[C]//International Conference on Machine Learning. 2018: 5085-5094.
Manders J, Marchiori E, van Laarhoven T. Simple domain adaptation with class prediction uncertainty alignment[J]. arXiv preprint arXiv:1804.04448, 2018, 1(2): 3.
Xu R, Li G, Yang J, et al. Unsupervised domain adaptation: An adaptive feature norm approach[J]. Preprint, 2018.
Shen J, Qu Y, Zhang W, et al. Wasserstein distance guided representation learning for domain adaptation[C]//Thirty-Second AAAI Conference on Artificial Intelligence. 2018.

2017

Tzeng E, Hoffman J, Saenko K, et al. Adversarial discriminative domain adaptation[C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017: 7167-7176.
ADDA
Shen J, Qu Y, Zhang W, et al. Adversarial representation learning for domain adaptation[J]. stat, 2017, 1050: 5.
对抗迁移学习
Haeusser P, Frerix T, Mordvintsev A, et al. Associative domain adaptation[C]//Proceedings of the IEEE International Conference on Computer Vision. 2017: 2765-2773.
Cariucci F M, Porzi L, Caputo B, et al. Autodial: Automatic domain alignment layers[C]//2017 IEEE International Conference on Computer Vision (ICCV). IEEE, 2017: 5077-5085.
Zellinger W, Grubinger T, Lughofer E, et al. Central moment discrepancy (cmd) for domain-invariant representation learning[J]. arXiv preprint arXiv:1702.08811, 2017.
CMD
Morerio P, Murino V. Correlation alignment by riemannian metric for domain adaptation[J]. arXiv preprint arXiv:1705.08180, 2017.
Long M, Zhu H, Wang J, et al. Deep transfer learning with joint adaptation networks[C]//Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR. org, 2017: 2208-2217.
JDN
Zellinger W , Moser B A , Grubinger T , et al. Robust Unsupervised Domain Adaptation for Neural Networks via Moment Alignment[J]. 2017.
French G, Mackiewicz M, Fisher M. Self-ensembling for visual domain adaptation[J]. arXiv preprint arXiv:1706.05208, 2017.
Motiian S, Piccirilli M, Adjeroh D A, et al. Unified deep supervised domain adaptation and generalization[C]//Proceedings of the IEEE International Conference on Computer Vision. 2017: 5715-5725.

2016

Sun B, Saenko K. Deep coral: Correlation alignment for deep domain adaptation[C]//European conference on computer vision. Springer, Cham, 2016: 443-450.
Ghifary M, Kleijn W B, Zhang M, et al. Deep reconstruction-classification networks for unsupervised domain adaptation[C]//European Conference on Computer Vision. Springer, Cham, 2016: 597-613.
Bousmalis K, Trigeorgis G, Silberman N, et al. Domain separation networks[C]//Advances in neural information processing systems. 2016: 343-351.
Ganin Y, Ustinova E, Ajakan H, et al. Domain-adversarial training of neural networks[J]. The Journal of Machine Learning Research, 2016, 17(1): 2096-2030.
对抗迁移学习
Li Y , Wang N , Shi J , et al. Adaptive Batch Normalization for practical domain adaptation[J]. Pattern Recognition, 2016, 80.
Long M, Zhu H, Wang J, et al. Unsupervised domain adaptation with residual transfer networks[C]//Advances in neural information processing systems. 2016: 136-144.
Sun B, Feng J, Saenko K. Return of frustratingly easy domain adaptation[C]//Thirtieth AAAI Conference on Artificial Intelligence. 2016.

2015

Zhang X, Yu F X, Chang S F, et al. Deep transfer network: Unsupervised domain adaptation[J]. arXiv preprint arXiv:1503.00591, 2015.
Ghifary M, Bastiaan Kleijn W, Zhang M, et al. Domain generalization for object recognition with multi-task autoencoders[C]//Proceedings of the IEEE international conference on computer vision. 2015: 2551-2559.
Long M, Cao Y, Wang J, et al. Learning transferable features with deep adaptation networks[J]. arXiv preprint arXiv:1502.02791, 2015.
Tzeng E, Hoffman J, Darrell T, et al. Simultaneous deep transfer across domains and tasks[C]//Proceedings of the IEEE International Conference on Computer Vision. 2015: 4068-4076.

2014

Tzeng E, Hoffman J, Zhang N, et al. Deep domain confusion: Maximizing for domain invariance[J]. arXiv preprint arXiv:1412.3474, 2014.
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.
MMD
Ajakan H, Germain P, Larochelle H, et al. Domain-adversarial neural networks[J]. arXiv preprint arXiv:1412.4446, 2014.

2013

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.