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Transfer learning has emerged as a powerful technique for improving the performance of machine learning models on new domains where labeled training data may be scarce.
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J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. Wortman, “Learning bounds for domain adaptation,” in Advances in neural information processing systems , 2008, pp. 129–136
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A. Daniely, R. Frostig, and Y. Singer, “Toward deeper understanding of neural networks: The power of initialization and a dual view on expressivity,” in Advances In Neural Information Processing Systems , 2016, pp. 2253–2261
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J. Shen, Y. Qu, W. Zhang, and Y. Yu, “Wasserstein distance guided representation learning for domain adaptation,” in Thirty-Second AAAI Conference on Artificial Intelligence , 2018
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K. You, X. Wang, M. Long, and M. Jordan, “Towards accurate model selection in deep unsupervised domain adaptation,” in International Conference on Machine Learning , 2019, pp. 7124–7133
2019
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X. Chen, S. Wang, M. Long, and J. Wang, “Transferability vs. discriminability: Batch spectral penalization for adversarial domain adaptation,” in International Conference on Machine Learning , 2019, pp. 1081–1090
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S. Hanneke and S. Kpotufe, “On the value of target data in transfer learning,” in Advances in Neural Information Processing Systems , 2019, pp. 9867–9877
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M. J. Wainwright, High-dimensional statistics: A non-asymptotic viewpoint . Cambridge University Press, 2019, vol. 48
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https://github.com/z-fabian/transfer_lowerbounds_arXiv , 2020
2020
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2019
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2019
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