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Domain adaptation enables the learner to safely generalize into novel environments by mitigating domain shifts across distributions.
Semi-supervised learning by entropy minimization
Y. Grandvalet and Y. Bengio · 2005
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Integrating structured biological data by kernel maximum mean discrepancy
K. M. Borgwardt, A. Gretton, M. J. Rasch, H.-P. Kriegel, B. Schölkopf, and A. J. Smola · 2006
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Visualizing data using t-sne
L. v. d. Maaten and G. Hinton · 2008
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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A theory of learning from different domains
S. Ben-David, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. W. Vaughan · 2010
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Impossibility theorems for domain adaptation
S. Ben-David, T. Lu, T. Luu, and D. Pál · 2010
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A survey on transfer learning
S. J. Pan and Q. Yang · 2010
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Adapting visual category models to new domains
K. Saenko, B. Kulis, M. Fritz, and T. Darrell · 2010
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Unbiased look at dataset bias
A. Torralba and A. Efros · 2011
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Geodesic flow kernel for unsupervised domain adaptation
B. Gong, Y. Shi, F. Sha, and K. Grauman · 2012
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Learning kernels for unsupervised domain adaptation with applications to visual object recognition
B. Gong, K. Grauman, and F. Sha · 2014
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Microsoft coco: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
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Dropout: a simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
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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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How transferable are features in deep neural networks?
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson · 2014
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Unsupervised domain adaptation by backpropagation
Y. Ganin and V. Lempitsky · 2015
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Learning transferable features with deep adaptation networks
M. Long, Y. Cao, J. Wang, and M. Jordan · 2015
Cited alongside, same era.
Simultaneous deep transfer across domains and tasks
E. Tzeng, J. Hoffman, T. Darrell, and K. Saenko · 2015
Cited alongside, same era.
Domain separation networks
K. Bousmalis, G. Trigeorgis, N. Silberman, D. Krishnan, and D. Erhan · 2016
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Domain-adversarial training of neural networks
Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky · 2016
Cited alongside, same era.
Deep reconstruction-classification networks for unsupervised domain adaptation
M. Ghifary, W. B. Kleijn, M. Zhang, D. Balduzzi, and W. Li · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Asymmetric tri-training for unsupervised domain adaptation
K. Saito, Y. Ushiku, and T. Harada · 2017
Later among the works it cites.
Adversarial discriminative domain adaptation
E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell · 2017
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Deep hashing network for unsupervised domain adaptation
H. Venkateswara, J. Eusebio, S. Chakraborty, and S. Panchanathan · 2017
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Partial transfer learning with selective adversarial networks
Z. Cao, M. Long, J. Wang, and M. I. Jordan · 2018
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Partial adversarial domain adaptation
Z. Cao, L. Ma, M. Long, and J. Wang · 2018
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Cycada: Cycle-consistent adversarial domain adaptation
J. Hoffman, E. Tzeng, T. Park, J.-Y. Zhu, P. Isola, K. Saenko, A. Efros, and T. Darrell · 2018
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Unsupervised domain adaptation with residual transfer networks
M. Long, H. Zhu, J. Wang, and M. I. Jordan · 2016
Cited alongside, same era.
Return of frustratingly easy domain adaptation
B. Sun, J. Feng, and K. Saenko · 2016
Cited alongside, same era.
Deep coral: Correlation alignment for deep domain adaptation
B. Sun and K. Saenko · 2016
Cited alongside, same era.
A discriminative feature learning approach for deep face recognition
Y. Wen, K. Zhang, Z. Li, and Y. Qiao · 2016
Cited alongside, same era.
Unsupervised pixel-level domain adaptation with generative adversarial networks
K. Bousmalis, N. Silberman, D. Dohan, D. Erhan, and D. Krishnan · 2017
Cited alongside, same era.
Unsupervised image-to-image translation networks
M.-Y. Liu, T. Breuel, and J. Kautz · 2017
Cited alongside, same era.
X. Li, S. Chen, X. Hu, and J. Yang · 2018
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Transferable representation learning with deep adaptation networks
M. Long, Y. Cao, Z. Cao, J. Wang, and M. I. Jordan · 2018
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Conditional adversarial domain adaptation
M. Long, Z. Cao, J. Wang, and M. I. Jordan · 2018
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From source to target and back: symmetric bi-directional adaptive gan
P. Russo, F. M. Carlucci, T. Tommasi, and B. Caputo · 2018
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Maximum classifier discrepancy for unsupervised domain adaptation
K. Saito, K. Watanabe, Y. Ushiku, and T. Harada · 2018
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Generate to adapt: Aligning domains using generative adversarial networks
S. Sankaranarayanan, Y. Balaji, C. D. Castillo, and R. Chellappa · 2018
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A dirt-t approach to unsupervised domain adaptation
R. Shu, H. H. Bui, H. Narui, and S. Ermon · 2018
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Characterizing and avoiding negative transfer
Z. Wang, Z. Dai, B. Póczos, and J. Carbonell · 2018
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Rethinking the smaller-norm-less-informative assumption in channel pruning of convolution layers
J. Ye, X. Lu, Z. Lin, and J. Z. Wang · 2018
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Collaborative and adversarial network for unsupervised domain adaptation
W. Zhang, W. Ouyang, W. Li, and D. Xu · 2018
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Ring loss: Convex feature normalization for face recognition
Y. Zheng, D. K. Pal, and M. Savvides · 2018
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