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Transfer learning (TL) utilizes data or knowledge from one or more source domains to facilitate the learning in a target domain.
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2006
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W. Dai, Q. Yang, G.-R. Xue, and Y. Yu, “Boosting for transfer learning,” in Proc. 24th Int’l Conf. on Machine learning , Corvallis, OR, Jun. 2007, pp. 193–200
2007
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S. Ben-David, J. Blitzer, K. Crammer, and F. Pereira, “Analysis of representations for domain adaptation,” in Proc. Advances in Neural Information Processing Systems , Vancouver, Canada, Dec. 2007, pp. 137–144
2007
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S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Trans. on Knowledge and Data Engineering , vol. 22, no. 10, pp. 1345–1359, 2009
2009
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S. Si, D. Tao, and B. Geng, “Bregman divergence-based regularization for transfer subspace learning,” IEEE Trans. on Knowledge and Data Engineering , vol. 22, no. 7, pp. 929–942, 2009
2009
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B. Cao, S. J. Pan, Y. Zhang, D.-Y. Yeung, and Q. Yang, “Adaptive transfer learning,” in Proc. 24th AAAI Conf. on Artificial Intelligence , vol. 2, no. 5, Atlanta, GA, Jul. 2010, p. 7
2010
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Y. Yao and G. Doretto, “Boosting for transfer learning with multiple sources,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition , San Francisco, CA, Jun. 2010, pp. 1855–1862
2010
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E. Eaton et al. , “Selective transfer between learning tasks using task-based boosting,” in Proc. 25th AAAI Conf. on Artificial Intelligence , San Francisco, CA, Aug. 2011, pp. 337–342
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2012
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C.-W. Seah, Y.-S. Ong, and I. W. Tsang, “Combating negative transfer from predictive distribution differences,” IEEE Trans. on Cybernetics , vol. 43, no. 4, pp. 1153–1165, 2012
2012
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M. Long, J. Wang, G. Ding, W. Cheng, X. Zhang, and W. Wang, “Dual transfer learning,” in Proc. 2012 SIAM Int’l Conf. on Data Mining , Brussels, Belgium, Dec. 2012, pp. 540–551
2012
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Y.-L. Yu and C. Szepesvári, “Analysis of kernel mean matching under covariate shift,” in Proc. 29th Int’l Conf. on Machine Learning , Edinburgh, Scotland, Jun. 2012, pp. 1147–1154
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2012
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M. Long, J. Wang, G. Ding, J. Sun, and P. S. Yu, “Transfer feature learning with joint distribution adaptation,” in Proc. IEEE Int’l Conf. on Computer Vision , Sydney, Australia, Dec. 2013, pp. 2200–2207
2013
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I. Kuzborskij and F. Orabona, “Stability and hypothesis transfer learning,” in Proc. 30th Int’l Conf. on Machine Learning , Atlanta, GA, Jun. 2013, pp. 942–950
2013
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J. Shi, M. Long, Q. Liu, G. Ding, and J. Wang, “Twin bridge transfer learning for sparse collaborative filtering,” in Proc. Pacific-Asia Conf. on Knowledge Discovery and Data Mining , Gold Coast, Australia, Apr. 2013, pp. 496–507
2013
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2013
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M. Ghifary, W. B. Kleijn, and M. Zhang, “Domain adaptive neural networks for object recognition,” in Proc. Pacific Rim Int’l Conf. on Artificial Intelligence , Queensland, Australia, Jun. 2014, pp. 898–904
2014
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P. Agrawal, R. Girshick, and J. Malik, “Analyzing the performance of multilayer neural networks for object recognition,” in Proc. European Conf. on Computer Vision , Zurich, Switzerland, September 2014, pp. 329–344
2014
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J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell, “DeCAF: A deep convolutional activation feature for generic visual recognition,” in Proc. 31st Int’l Conf. on Machine Learning , Beijing, China, Jun. 2014, pp. 647–655
2014
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P. Agrawal, R. Girshick, and J. Malik, “Analyzing the performance of multilayer neural networks for object recognition,” in Proc. European Conf. on Computer Vision , Zurich, Switzerland, 2014, pp. 329–344
2014
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D. Wu, B. J. Lance, and V. J. Lawhern, “Transfer learning and active transfer learning for reducing calibration data in single-trial classification of visually-evoked potentials,” in Proc. IEEE Int’l Conf. on Systems, Man, and Cybernetics , San Diego, CA, October 2014
2014
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J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural networks?” in Proc. Advances in Neural Information Processing Systems , Montréal, Canada, Dec. 2014, pp. 3320–3328
2014
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M. Long, Y. Cao, J. Wang, and M. Jordan, “Learning transferable features with deep adaptation networks,” in Proc. 32nd Int’l Conf. on Machine Learning , Lille, France, Jul. 2015, pp. 97–105
2015
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B. Tan, Y. Song, E. Zhong, and Q. Yang, “Transitive transfer learning,” in Proc. 21st ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining , Sydney, Australia, Aug. 2015, pp. 1155–1164
2015
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Y. Xu, X. Fang, J. Wu, X. Li, and D. Zhang, “Discriminative transfer subspace learning via low-rank and sparse representation,” IEEE Trans. on Image Processing , vol. 25, no. 2, pp. 850–863, 2015
2015
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S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in Proc. 32nd Int’l Conf. on Machine Learning , Lille, France, Jul. 2015, pp. 448–456
2015
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Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky, “Domain-adversarial training of neural networks,” Journal of Machine Learning Research , vol. 17, no. 1, pp. 2096–2030, 2016
2016
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M. Xie, N. Jean, M. Burke, D. Lobell, and S. Ermon, “Transfer learning from deep features for remote sensing and poverty mapping,” in Proc. 30th AAAI Conf. on Artificial Intelligence , no. 7, Phoenix, AZ, February 2016, pp. 3929–3935
2016
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D. Wu, V. J. Lawhern, W. D. Hairston, and B. J. Lance, “Switching EEG headsets made easy: Reducing offline calibration effort using active wighted adaptation regularization,” IEEE Trans. on Neural Systems and Rehabilitation Engineering , vol. 24, no. 11, pp. 1125–1137, 2016
2016
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2016
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J. Zhang, W. Li, and P. Ogunbona, “Joint geometrical and statistical alignment for visual domain adaptation,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition , Honolulu, HI, Jul. 2017, pp. 1859–1867
2017
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D. Wu, “Online and offline domain adaptation for reducing BCI calibration effort,” IEEE Trans. on Human-Machine Systems , vol. 47, no. 4, pp. 550–563, 2017
2017
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Y.-P. Lin and T.-P. Jung, “Improving EEG-based emotion classification using conditional transfer learning,” Frontiers in Human Neuroscience , vol. 11, p. 334, 2017
2017
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G. Xie, Y. Sun, M. Lin, and K. Tang, “A selective transfer learning method for concept drift adaptation,” in Proc. Int’l Symposium on Neural Networks , Hokkaido, Japan, Jun. 2017, pp. 353–361
2017
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B. Tan, Y. Zhang, S. J. Pan, and Q. Yang, “Distant domain transfer learning,” in Proc. 31st AAAI Conf. on Artificial Intelligence , San Francisco, CA, Feb. 2017, pp. 2604–2610
2017
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2019
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A. Chaudhry, M. Ranzato, M. Rohrbach, and M. Elhoseiny, “Efficient lifelong learning with A-GEM,” in Proc. Int’l Conf. on Learning Representations , New Orleans, LA, May 2019
2019
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2017
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2017
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D. Lopez-Paz and M. Ranzato, “Gradient episodic memory for continual learning,” in Proc. Advances in Neural Information Processing Systems , Long Beach, CA, Dec. 2017, pp. 6467–6476
2017
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C. Tan, F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu, “A survey on deep transfer learning,” in Proc. Int’l Conf. on Artificial Neural Networks , Rhodes, Greece, Oct. 2018, pp. 270–279
2018
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M. Abdullah Jamal, H. Li, and B. Gong, “Deep face detector adaptation without negative transfer or catastrophic forgetting,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition , Salt Lake City, Utah, Jun. 2018, pp. 5608–5618
2018
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H. Yoon and J. Li, “A novel positive transfer learning approach for telemonitoring of Parkinson’s disease,” IEEE Trans. on Automation Science and Engineering , vol. 16, no. 1, pp. 180–191, 2018
2018
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R. Xu, Z. Chen, W. Zuo, J. Yan, and L. Lin, “Deep cocktail network: Multi-source unsupervised domain adaptation with category shift,” in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition , Salt Lake City, Utah, Jun. 2018, pp. 3964–3973
2018
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M. J. Afridi, A. Ross, and E. M. Shapiro, “On automated source selection for transfer learning in convolutional neural networks,” Pattern Recognition , vol. 73, pp. 65–75, 2018
2018
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S. Cheng, Y. Dong, T. Pang, H. Su, and J. Zhu, “Improving black-box adversarial attacks with a transfer-based prior,” in Proc. Advances in Neural Information Processing Systems , Vancouver, Canada, Dec. 2019
2019
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H. Tang and K. Jia, “Discriminative adversarial domain adaptation.” in Proc. 34th AAAI Conf. on Artificial Intelligence , New York, NY, Feb. 2020, pp. 5940–5947
2020
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2020
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2020
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W. Zhang and D. Wu, “Manifold embedded knowledge transfer for brain-computer interfaces,” IEEE Trans. on Neural Systems and Rehabilitation Engineering , vol. 28, no. 5, pp. 1117–1127, 2020
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A. Abdelkader, M. J. Curry, L. Fowl, T. Goldstein, A. Schwarzschild, M. Shu, C. Studer, and C. Zhu, “Headless horseman: Adversarial attacks on transfer learning models,” in Proc. Int’l Conf. on Acoustics, Speech and Signal Processing , Barcelona, Spain, May 2020, pp. 3087–3091
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K. Liang, J. Y. Zhang, O. Koyejo, and B. Li, “Does adversarial transferability indicate knowledge transferability?” in Proc. 38th Int’l Conf. on Machine Learning , 2021
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