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The effectiveness of generative adversarial approaches in producing images according to a specific style or visual domain has recently opened new directions to solve the unsupervised domain adaptation problem.
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Visualizing data using t-sne
L. v. d. Maaten and G. Hinton · 2008
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Domain adaptation problems: A dasvm classification technique and a circular validation strategy
L. Bruzzone and M. Marconcini · 2010
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Contour detection and hierarchical image segmentation
P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik · 2011
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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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The German traffic sign recognition benchmark: a multi-class classification competition
J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel · 2011
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Iterative self-labeling domain adaptation for linear structured image classification
A. Habrard, J.-P. Peyrache, and M. Sebban · 2013
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Evaluation of traffic sign recognition methods trained on synthetically generated data
B. Moiseev, A. Konev, A. Chigorin, and A. Konushin · 2013
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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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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2015
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Domain adaptation of weighted majority votes via perturbed variation-based self-labeling
E. Morvant · 2015
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Simultaneous deep transfer across domains and tasks
E. Tzeng, J. Hoffman, T. Darrell, and K. Saenko · 2015
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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
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Image style transfer using convolutional neural networks
L. A. Gatys, A. S. Ecker, and M. Bethge · 2016
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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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Unsupervised pixel-level domain adaptation with gans
K. Bousmalis, N. Silberman, D. Dohan, D. Erhan, and D. Krishnan · 2017
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Autodial: Automatic domain alignment layers
F. M. Carlucci, L. Porzi, B. Caputo, E. Ricci, and S. Rota Bulò · 2017
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Associative domain adaptation
P. Haeusser, T. Frerix, A. Mordvintsev, and D. Cremers · 2017
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Learning to discover cross-domain relations with generative adversarial networks
T. Kim, M. Cha, H. Kim, J. K. Lee, and J. Kim · 2017
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Unsupervised image-to-image translation networks
M.-Y. Liu, T. Breuel, and J. Kautz · 2017
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Conditional image synthesis with auxiliary classifier GANs
A. Odena, C. Olah, and J. Shlens · 2017
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Perceptual losses for real-time style transfer and super-resolution
J. Johnson, A. Alahi, and L. Fei-Fei · 2016
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Coupled generative adversarial networks
M.-Y. Liu and O. Tuzel · 2016
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Multi-class generative adversarial networks with the l2 loss function
X. Mao, Q. Li, H. Xie, R. Y. Lau, and Z. Wang · 2016
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Improved techniques for training gans
T. Salimans, I. J. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2016
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Learning transferrable representations for unsupervised domain adaptation
O. Sener, H. O. Song, A. Saxena, and S. Savarese · 2016
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Return of frustratingly easy domain adaptation
B. Sun, J. Feng, and K. Saenko · 2016
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Asymmetric tri-training for unsupervised domain adaptation
K. Saito, Y. Ushiku, and T. Harada · 2017
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Generate to adapt: Aligning domains using generative adversarial networks
S. Sankaranarayanan, Y. Balaji, C. D. Castillo, and R. Chellappa · 2017
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Learning from simulated and unsupervised images through adversarial training
A. Shrivastava, T. Pfister, O. Tuzel, J. Susskind, W. Wang, and R. Webb · 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, T. Darrell, and K. Saenko · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros · 2017
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