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The recent success of deep neural networks relies on massive amounts of labeled data.
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A theory of learning from different domains
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Domain adaptation via transfer component analysis
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X. Glorot, A. Bordes, and Y. Bengio · 2011
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J. Ngiam, A. Khosla, M. Kim, J. Nam, H. Lee, and A. Y. Ng · 2011
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Unbiased look at dataset bias
A. Torralba and A. A. Efros · 2011
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L. Duan, I. W. Tsang, and D. Xu · 2012
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Visual event recognition in videos by learning from web data
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Imagenet classification with deep convolutional neural networks
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How transferable are features in deep neural networks?
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Deep domain confusion: Maximizing for domain invariance
E. Tzeng, J. Hoffman, N. Zhang, K. Saenko, and T. Darrell · 2014
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Flexible transfer learning under support and model shift
X. Wang and J. Schneider · 2014
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LSDA: Large scale detection through adaptation
J. Hoffman, S. Guadarrama, E. Tzeng, R. Hu, J. Donahue, R. Girshick, T. Darrell, and K. Saenko · 2014
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Learning transferable features with deep adaptation networks
M. Long, Y. Cao, J. Wang, and M. I. Jordan · 2015
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Unsupervised domain adaptation by backpropagation
Y. Ganin and V. Lempitsky · 2015
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Representation learning: A review and new perspectives
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Bilinear cnn models for fine-grained visual recognition
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Return of frustratingly easy domain adaptation
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