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Unsupervised domain adaptation (uDA) models focus on pairwise adaptation settings where there is a single, labeled, source and a single target domain.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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The im algorithm: a variational approach to information maximization
D. Barber and F. Agakov · 2003
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Visualizing data using t-sne
L. v. d. Maaten and G. Hinton · 2008
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Factorized orthogonal latent spaces
M. Salzmann, C. H. Ek, R. Urtasun, and T. Darrell · 2010
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Reading digits in natural images with unsupervised feature learning
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng · 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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Unsupervised visual domain adaptation using subspace alignment
B. Fernando, A. Habrard, M. Sebban, and T. Tuytelaars · 2013
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Transfer joint matching for unsupervised domain adaptation
M. Long, J. Wang, G. Ding, J. Sun, and P. S. Yu · 2014
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Generating sentences from a continuous space
S. R. Bowman, L. Vilnis, O. Vinyals, A. M. Dai, R. Jozefowicz, and S. Bengio · 2015
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Unsupervised domain adaptation by backpropagation
Y. Ganin and V. Lempitsky · 2015
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
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Unsupervised domain adaptation for zero-shot learning
E. Kodirov, T. Xiang, Z. Fu, and S. Gong · 2015
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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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Variational information maximisation for intrinsically motivated reinforcement learning
S. Mohamed and D. J. Rezende · 2015
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Deep learning and the information bottleneck principle
N. Tishby and N. Zaslavsky · 2015
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Deep transfer network: Unsupervised domain adaptation
X. Zhang, F. X. Yu, S.-F. Chang, and S. Wang · 2015
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Deep variational information bottleneck
A. A. Alemi, I. Fischer, J. V. Dillon, and K. Murphy · 2016
Cited alongside, same era.
Domain separation networks
K. Bousmalis, G. Trigeorgis, N. Silberman, D. Krishnan, and D. Erhan · 2016
Cited alongside, same era.
Relevant sparse codes with variational information bottleneck
M. Chalk, O. Marre, and G. Tkacik · 2016
Cited alongside, same era.
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.
Coupled generative adversarial networks
M.-Y. Liu and O. Tuzel · 2016
Cited alongside, same era.
Deep learning of transferable representation for scalable domain adaptation
M. Long, J. Wang, Y. Cao, J. Sun, and S. Y. Philip · 2016
Cited alongside, same era.
A comprehensive survey on domain adaptation for visual applications
G. Csurka · 2017
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Deeper, broader and artier domain generalization
D. Li, Y. Yang, Y.-Z. Song, and T. M. Hospedales · 2017
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Unsupervised image-to-image translation networks
M.-Y. Liu, T. Breuel, and J. Kautz · 2017
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Few-shot adversarial domain adaptation
S. Motiian, Q. Jones, S. Iranmanesh, and G. Doretto · 2017
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Adversarial symmetric variational autoencoder
Y. Pu, W. Wang, R. Henao, L. Chen, Z. Gan, C. Li, and L. Carin · 2017
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Learning multiple visual domains with residual adapters
S.-A. Rebuffi, H. Bilen, and A. Vedaldi · 2017
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Deep coral: Correlation alignment for deep domain adaptation
B. Sun and K. Saenko · 2016
Cited alongside, same era.
Return of frustratingly easy domain adaptation
B. Sun, J. Feng, and K. Saenko · 2016
Cited alongside, same era.
Pixel-level domain transfer
D. Yoo, N. Kim, S. Park, A. S. Paek, and I. S. Kweon · 2016
Cited alongside, same era.
Infinite variational autoencoder for semi-supervised learning
M. E. Abbasnejad, A. Dick, and A. van den Hengel · 2017
Cited alongside, same era.
Combogan: Unrestrained scalability for image domain translation
A. Anoosheh, E. Agustsson, R. Timofte, and L. Van Gool · 2017
Cited alongside, same era.
One-sided unsupervised domain mapping
S. Benaim and L. Wolf · 2017
Cited alongside, same era.
Asymmetric tri-training for unsupervised domain adaptation
K. Saito, Y. Ushiku, and T. Harada · 2017
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Adversarial discriminative domain adaptation
E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell · 2017
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Mind the class weight bias: Weighted maximum mean discrepancy for unsupervised domain adaptation
H. Yan, Y. Ding, P. Li, Q. Wang, Y. Xu, and W. Zuo · 2017
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Central moment discrepancy (cmd) for domain-invariant representation learning
W. Zellinger, T. Grubinger, E. Lughofer, T. Natschläger, and S. Saminger-Platz · 2017
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Joint geometrical and statistical alignment for visual domain adaptation
J. Zhang, W. Li, and P. Ogunbona · 2017
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Multiple source domain adaptation with adversarial training of neural networks
H. Zhao, S. Zhang, G. Wu, J. P. Costeira, J. Moura, and G. J. Gordon · 2017
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Information dropout: Learning optimal representations through noisy computation
A. Achille and S. Soatto · 2018
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Mixgan: Learning concepts from different domains for mixture generation
G.-Y. Hao, H.-X. Yu, and W.-S. Zheng · 2018
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Stargan-vc: Non-parallel many-to-many voice conversion with star generative adversarial networks
H. Kameoka, T. Kaneko, K. Tanaka, and N. Hojo · 2018
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