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Visual Domain Adaptation is a problem of immense importance in computer vision.
Analysis of representations for domain adaptation
S. Ben-David, J. Blitzer, K. Crammer, and F. Pereira · 2007
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Frustratingly easy domain adaptation
H. Daume III · 2007
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Semantic object classes in video: A high-definition ground truth database
G. J. Brostow, J. Fauqueur, and R. Cipolla · 2009
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The pascal visual object classes (voc) challenge
M. Everingham, L. Gool, C. K. Williams, J. Winn, and A. Zisserman · 2010
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Daml: Domain adaptation metric learning
B. Geng, D. Tao, and C. Xu · 2011
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Domain adaptation for object recognition: An unsupervised approach
R. Gopalan, R. Li, and R. Chellappa · 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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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Unsupervised domain adaptation by backpropagation
Y. Ganin and V. Lempitsky · 2014
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
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Microsoft coco: Common objects in context
T.-Y. Lin et al · 2014
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The role of context for object detection and semantic segmentation in the wild
R. Mottaghi, X. Chen, X. Liu, N.-G. Cho, S.-W. Lee, S. Fidler, R. Urtasun, and A. Yuille · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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A neural algorithm of artistic style
L. A. Gatys, A. S. Ecker, and M. Bethge · 2015
Cited alongside, same era.
Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
Cited alongside, same era.
Learning transferable features with deep adaptation networks
M. Long, Y. Cao, J. Wang, and M. I. Jordan · 2015
Cited alongside, same era.
Coupled generative adversarial networks
M.-Y. Liu and O. Tuzel · 2016
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Unsupervised domain adaptation with residual transfer networks
M. Long, J. Wang, and M. I. Jordan · 2016
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Hierarchical question-image co-attention for visual question answering
J. Lu, J. Yang, D. Batra, and D. Parikh · 2016
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Playing for data: Ground truth from computer games
S. R. Richter, V. Vineet, S. Roth, and V. Koltun · 2016
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The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes
G. Ros, L. Sellart, J. Materzynska, D. Vazquez, and A. M. Lopez · 2016
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Multi-scale context aggregation by dilated convolutions
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A. Radford, L. Metz, and S. Chintala · 2015
Cited alongside, same era.
Unsupervised pixel-level domain adaptation with generative adversarial networks
K. Bousmalis, N. Silberman, D. Dohan, D. Erhan, and D. Krishnan · 2016
Cited alongside, same era.
The cityscapes dataset for semantic urban scene understanding
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele · 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.
Fcns in the wild: Pixel-level adversarial and constraint-based adaptation
J. Hoffman, D. Wang, F. Yu, and T. Darrell · 2016
Cited alongside, same era.
Perceptual losses for real-time style transfer and super-resolution
J. Johnson, A. Alahi, and L. Fei-Fei · 2016
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F. Yu and V. Koltun · 2016
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Cycada: Cycle-consistent adversarial domain adaptation
J. Hoffman, E. Tzeng, T. Park, J.-Y. Zhu, P. Isola, K. Saenko, A. A. Efros, and T. Darrell · 2017
Closest in time.
Generate to adapt: Aligning domains using generative adversarial networks
S. Sankaranarayanan, Y. Balaji, C. D. Castillo, and R. Chellappa · 2017
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Adversarial discriminative domain adaptation
E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell · 2017
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Curriculum domain adaptation for semantic segmentation of urban scenes
Y. Zhang, P. David, and B. Gong · 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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