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In this work, we present a method for unsupervised domain adaptation.
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K. Bousmalis, G. Trigeorgis, N. Silberman, D. Krishnan, and D. Erhan · 2016
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The cityscapes dataset for semantic urban scene understanding
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Deep reconstruction-classification networks for unsupervised domain adaptation
M. Ghifary, W. B. Kleijn, M. Zhang, D. Balduzzi, and W. Li · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Fcns in the wild: Pixel-level adversarial and constraint-based adaptation
J. Hoffman, D. Wang, F. Yu, and T. Darrell · 2016
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M.-Y. Liu and O. Tuzel · 2016
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P. Haeusser, T. Frerix, A. Mordvintsev, and D. Cremers · 2017
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Visda: The visual domain adaptation challenge
X. Peng, B. Usman, N. Kaushik, J. Hoffman, D. Wang, and K. Saenko · 2017
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Variational recurrent adversarial deep domain adaptation
S. Purushotham, W. Carvalho, T. Nilanon, and Y. Liu · 2017
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Asymmetric tri-training for unsupervised domain adaptation
K. Saito, Y. Ushiku, and T. Harada · 2017
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Unsupervised cross-domain image generation
Y. Taigman, A. Polyak, and L. Wolf · 2017
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Adversarial discriminative domain adaptation
E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell · 2017
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Dilated residual networks
F. Yu, V. Koltun, and T. Funkhouser · 2017
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Curriculum domain adaptation for semantic segmentation of urban scenes
Y. Zhang, P. David, and B. Gong · 2017
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