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Enhancing feature transferability by matching marginal distributions has led to improvements in domain adaptation, although this is at the expense of feature discrimination.
Joint semantic domain alignment and target classifier learning for unsupervised domain adaptation
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Deep transfer learning with joint adaptation networks
M. Long, H. Zhu, J. Wang, and M. I. Jordan · 2017
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Adversarial discriminative domain adaptation
E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell · 2017
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Deep clustering for unsupervised learning of visual features
M. Caron, P. Bojanowski, A. Joulin, and M. Douze · 2018
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Self-ensembling for visual domain adaptation
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S. Dai, Y. Cheng, Y. Zhang, Z. Gan, J. Liu, and L. Carin · 2019
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Z. Deng, Y. Luo, and J. Zhu · 2019
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A simple framework for contrastive learning of visual representations
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton · 2020
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