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Unsupervised Domain Adaptation (UDA) has attracted a lot of attention in the last ten years.
Shimodaira, H.: Improving predictive inference under covariate shift by weighting the log-likelihood function. Journal of statistical planning and inference 90
2000
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Grandvalet, Y., Bengio, Y.: Semi-supervised learning by entropy minimization. In: Advances in neural information processing systems. pp. 529–536 (2005)
2005
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Ben-David, S., Blitzer, J., Crammer, K., Pereira, F.: Analysis of representations for domain adaptation. In: Advances in neural information processing systems. pp. 137–144 (2007)
2007
Earlier work this paper cites.
Huang, J., Gretton, A., Borgwardt, K., Schölkopf, B., Smola, A.J.: Correcting sample selection bias by unlabeled data. In: Advances in neural information processing systems. pp. 601–608 (2007)
2007
Earlier work this paper cites.
Sugiyama, M., Krauledat, M., MÞller, K.R.: Covariate shift adaptation by importance weighted cross validation. Journal of Machine Learning Research 8
2007
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Maaten, L.v.d., Hinton, G.: Visualizing data using t-sne. Journal of machine learning research 9
2008
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Gretton, A., Smola, A., Huang, J., Schmittfull, M., Borgwardt, K., Schölkopf, B.: Covariate shift by kernel mean matching. Dataset shift in machine learning 3
2009
Earlier work this paper cites.
Mansour, Y., Mohri, M., Rostamizadeh, A.: Domain adaptation: Learning bounds and algorithms. In: 22nd Conference on Learning Theory, COLT 2009 (2009)
2009
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Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Transactions on knowledge and data engineering 22
2009
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Quionero-Candela, J., Sugiyama, M., Schwaighofer, A., Lawrence, N.D.: Dataset shift in machine learning. The MIT Press (2009)
2009
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Storkey, A.: When training and test sets are different: characterizing learning transfer. Dataset shift in machine learning pp. 3–28 (2009)
2009
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Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., Vaughan, J.W.: A theory of learning from different domains. Machine learning 79
2010
Earlier work this paper cites.
Cortes, C., Mansour, Y., Mohri, M.: Learning bounds for importance weighting. In: Advances in neural information processing systems. pp. 442–450 (2010)
2010
Earlier work this paper cites.
Gretton, A., Borgwardt, K.M., Rasch, M.J., Schölkopf, B., Smola, A.: A kernel two-sample test. Journal of Machine Learning Research 13
2012
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Zhang, K., Schölkopf, B., Muandet, K., Wang, Z.: Domain adaptation under target and conditional shift. In: International Conference on Machine Learning. pp. 819–827 (2013)
2013
Cited alongside, same era.
Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: International Conference on Machine Learning. pp. 1180–1189 (2015)
2015
Cited alongside, same era.
Long, M., Cao, Y., Wang, J., Jordan, M.I.: Learning transferable features with deep adaptation networks. In: Proceedings of the 32nd International Conference on International Conference on Machine Learning-Volume 37. pp. 97–105. JMLR. org (2015)
2015
Cited alongside, same era.
2016
Cited alongside, same era.
Long, M., Cao, Z., Wang, J., Jordan, M.I.: Conditional adversarial domain adaptation. In: Advances in Neural Information Processing Systems. pp. 1640–1650 (2018)
2018
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2018
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Shen, J., Qu, Y., Zhang, W., Yu, Y.: Wasserstein distance guided representation learning for domain adaptation. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
2018
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Zhang, J., Ding, Z., Li, W., Ogunbona, P.: Importance weighted adversarial nets for partial domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 8156–8164 (2018)
2018
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He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)
2016
Cited alongside, same era.
2017
Cited alongside, same era.
Long, M., Zhu, H., Wang, J., Jordan, M.I.: Deep transfer learning with joint adaptation networks. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70. pp. 2208–2217. JMLR. org (2017)
2017
Cited alongside, same era.
Beery, S., Van Horn, G., Perona, P.: Recognition in terra incognita. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 456–473 (2018)
2018
Cited alongside, same era.
Bhushan Damodaran, B., Kellenberger, B., Flamary, R., Tuia, D., Courty, N.: Deepjdot: Deep joint distribution optimal transport for unsupervised domain adaptation. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 447–463 (2018)
2018
Cited alongside, same era.
Bottou, L., Arjovsky, M., Lopez-Paz, D., Oquab, M.: Geometrical insights for implicit generative modeling. In: Braverman Readings in Machine Learning. Key Ideas from Inception to Current State, pp. 229–268. Springer (2018)
2018
Cited alongside, same era.
Cao, Y., Long, M., Wang, J.: Unsupervised domain adaptation with distribution matching machines. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
2018
Cited alongside, same era.
Cao, Z., Ma, L., Long, M., Wang, J.: Partial adversarial domain adaptation. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 135–150 (2018)
2018
Cited alongside, same era.
2019
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Geva, M., Goldberg, Y., Berant, J.: Are we modeling the task or the annotator? an investigation of annotator bias in natural language understanding datasets. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). pp. 1161–1166 (2019)
2019
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Johansson, F., Sontag, D., Ranganath, R.: Support and invertibility in domain-invariant representations. In: The 22nd International Conference on Artificial Intelligence and Statistics. pp. 527–536 (2019)
2019
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Liu, H., Long, M., Wang, J., Jordan, M.: Transferable adversarial training: A general approach to adapting deep classifiers. In: International Conference on Machine Learning. pp. 4013–4022 (2019)
2019
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Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems. pp. 8024–8035 (2019)
2019
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Wu, Y., Winston, E., Kaushik, D., Lipton, Z.: Domain adaptation with asymmetrically-relaxed distribution alignment. In: International Conference on Machine Learning. pp. 6872–6881 (2019)
2019
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You, K., Long, M., Cao, Z., Wang, J., Jordan, M.I.: Universal domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2720–2729 (2019)
2019
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Zhao, H., Des Combes, R.T., Zhang, K., Gordon, G.: On learning invariant representations for domain adaptation. In: International Conference on Machine Learning. pp. 7523–7532 (2019)
2019
Later among the works it cites.