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Current Domain Adaptation (DA) methods based on deep architectures assume that the source samples arise from a single distribution.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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The elements of statistical learning
J. Friedman, T. Hastie, and R. Tibshirani · 2001
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Correcting sample selection bias by unlabeled data
J. Huang, A. Gretton, K. M. Borgwardt, B. Schölkopf, and A. J. Smola · 2006
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Caltech-256 object category dataset
G. Griffin, A. Holub, and P. Perona · 2007
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Adapting svm classifiers to data with shifted distributions
J. Yang, R. Yan, and A. G. Hauptmann · 2007
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Domain adaptation from multiple sources via auxiliary classifiers
L. Duan, I. W. Tsang, D. Xu, and T.-S. Chua · 2009
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Domain adaptation: Learning bounds and algorithms
Y. Mansour, M. Mohri, and A. Rostamizadeh · 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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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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Contour detection and hierarchical image segmentation
P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik · 2011
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A two-stage weighting framework for multi-source domain adaptation
Q. Sun, R. Chattopadhyay, S. Panchanathan, and J. Ye · 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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Discovering latent domains for multisource domain adaptation
J. Hoffman, B. Kulis, T. Darrell, and K. Saenko · 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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No bias left behind: Covariate shift adaptation for discriminative 3d pose estimation
M. Yamada, L. Sigal, and M. Raptis · 2012
Cited alongside, same era.
Unsupervised visual domain adaptation using subspace alignment
B. Fernando, A. Habrard, M. Sebban, and T. Tuytelaars · 2013
Cited alongside, same era.
Connecting the dots with landmarks: Discriminatively learning domain-invariant features for unsupervised domain adaptation
B. Gong, K. Grauman, and F. Sha · 2013
Cited alongside, same era.
Reshaping visual datasets for domain adaptation
B. Gong, K. Grauman, and F. Sha · 2013
Cited alongside, same era.
Transfer sparse coding for robust image representation
M. Long, G. Ding, J. Wang, J. Sun, Y. Guo, and P. S. Yu · 2013
Cited alongside, same era.
Decaf: A deep convolutional activation feature for generic visual recognition
Lightweight unsupervised domain adaptation by convolutional filter reconstruction
R. Aljundi and T. Tuytelaars · 2016
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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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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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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Revisiting batch normalization for practical domain adaptation
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J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell · 2014
Cited alongside, same era.
Unsupervised adaptation across domain shifts by generating intermediate data representations
R. Gopalan, R. Li, and R. Chellappa · 2014
Cited alongside, same era.
Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 2014
Cited alongside, same era.
Latent domains modeling for visual domain adaptation
C. Xiong, S. McCloskey, S.-H. Hsieh, and J. J. Corso · 2014
Cited alongside, same era.
Deep learning of scene-specific classifier for pedestrian detection
X. Zeng, W. Ouyang, M. Wang, and X. Wang · 2014
Cited alongside, same era.
Unsupervised domain adaptation by backpropagation
Y. Ganin and V. Lempitsky · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Cited alongside, same era.
Y. Li, N. Wang, J. Shi, J. Liu, and X. Hou · 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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Deep coral: Correlation alignment for deep domain adaptation
B. Sun and K. Saenko · 2016
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Unsupervised pixel-level domain adaptation with generative adversarial networks
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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Just dial: Domain alignment layers for unsupervised domain adaptation
F. M. Carlucci, L. Porzi, B. Caputo, E. Ricci, and S. Rota Bulò · 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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Cross-domain recognition by identifying joint subspaces of source domain and target domain
Y. Lin, J. Chen, Y. Cao, Y. Zhou, L. Zhang, Y. Y. Tang, and S. Wang · 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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