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Large-scale labeled training datasets have enabled deep neural networks to excel on a wide range of benchmark vision tasks.
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Learning with augmented features for supervised and semi-supervised heterogeneous domain adaptation
Wen Li, Lixin Duan, Dong Xu, and Ivor W Tsang · 2014
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Artem Rozantsev, Mathieu Salzmann, and Pascal Fua · 2016
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Domain adaptation for visual applications: A comprehensive survey
Gabriela Csurka · 2017
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Cycada: Cycle-consistent adversarial domain adaptation
Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei A Efros, and Trevor Darrell · 2017
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Ming-Yu Liu and Oncel Tuzel · 2016
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Learning from simulated and unsupervised images through adversarial training
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Correlation alignment for unsupervised domain adaptation
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Adversarial discriminative domain adaptation
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
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Deep unsupervised convolutional domain adaptation
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Guoliang Kang, Liang Zheng, Yan Yan, and Yi Yang · 2018
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