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Transfer learning, which allows a source task to affect the inductive bias of the target task, is widely used in computer vision.
Clustering learning tasks and the selective cross-task transfer of knowledge
S. Thrun and J. O’Sullivan · 1998
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Correcting sample selection bias in maximum entropy density estimation
M. Dudík, S. J. Phillips, and R. E. Schapire · 2006
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Boosting for transfer learning
W.Dai, Q.Yang, G. Xue, and Y.Yu · 2007
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Cross-domain video concept detection using adaptive SVMs
J. Yang, R. Yan, and A. G. Hauptmann · 2007
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Automated flower classification over a large number of classes
M.-E. Nilsback and A. Zisserman · 2008
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Domain transfer SVM for video concept detection
L. Duan, I. W. Tsang, D. Xu, and S. J. Maybank · 2009
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A survey on transfer learning
S. J. Pan, Q. Yang, et al · 2010
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Learning with whom to share in multi-task feature learning
Z. Kang, K. Grauman, and F. Sha · 2011
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The caltech-ucsd birds-200-2011 dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
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Deep learning of representations for unsupervised and transfer learning
Y. Bengio · 2012
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How do humans sketch objects?
M. Eitz, J. Hays, and M. Alexa · 2012
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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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Learning task grouping and overlap in multi-task learning
A. Kumar and H. Daumé III · 2012
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Estimating or propagating gradients through stochastic neurons for conditional computation
Y. Bengio, N. Léonard, and A. Courville · 2013
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Dlid: Deep learning for domain adaptation by interpolating between domains
S. Chopra, S. Balakrishnan, and R. Gopalan · 2013
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3d object representations for fine-grained categorization
J. Krause, M. Stark, J. Deng, and L. Fei-Fei · 2013
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Domain adaptive object detection
F. Mirrashed, V. I. Morariu, B. Siddiquie, R. S. Feris, and L. S. Davis · 2013
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Decaf: A deep convolutional activation feature for generic visual recognition
J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell · 2014
Cited alongside, same era.
Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
Cited alongside, same era.
Cnn features off-the-shelf: an astounding baseline for recognition
A. Sharif Razavian, H. Azizpour, J. Sullivan, and S. Carlsson · 2014
Cited alongside, same era.
How transferable are features in deep neural networks?
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson · 2014
Cited alongside, same era.
Conditional computation in neural networks for faster models
E. Bengio, P.-L. Bacon, J. Pineau, and D. Precup · 2015
Cited alongside, same era.
Domain adaptation for visual applications: A comprehensive survey
G. Csurka · 2017
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Spatially adaptive computation time for residual networks
M. Figurnov, M. D. Collins, Y. Zhu, L. Zhang, J. Huang, D. P. Vetrov, and R. Salakhutdinov · 2017
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Borrowing treasures from the wealthy: Deep transfer learning through selective joint fine-tuning
W. Ge and Y. Yu · 2017
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Densely connected convolutional networks
G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger · 2017
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Learning without forgetting
Z. Li and D. Hoiem · 2017
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Fully-adaptive feature sharing in multi-task networks with applications in person attribute classification
Y. Lu, A. Kumar, S. Zhai, Y. Cheng, T. Javidi, and R. S. Feris · 2017
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Deep domain adaptation for describing people based on fine-grained clothing attributes
Q. Chen, J. Huang, R. Feris, L. M. Brown, J. Dong, and S. Yan · 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.
Large-scale classification of fine-art paintings: Learning the right metric on the right feature
B. Saleh and A. Elgammal · 2015
Cited alongside, same era.
Factors of transferability for a generic convnet representation
H. Azizpour, A. S. Razavian, J. Sullivan, A. Maki, and S. Carlsson · 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.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Categorical reparameterization with gumbel-softmax
E. Jang, S. Gu, and B. Poole · 2016
Cited alongside, same era.
Later among the works it cites.
Learning multiple visual domains with residual adapters
S.-A. Rebuffi, H. Bilen, and A. Vedaldi · 2017
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Incremental learning through deep adaptation
A. Rosenfeld and J. K. Tsotsos · 2017
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Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
N. Shazeer, A. Mirhoseini, K. Maziarz, A. Davis, Q. Le, G. Hinton, and J. Dean · 2017
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Do better imagenet models transfer better?
S. Kornblith, J. Shlens, and Q. V. Le · 2018
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Co-regularized alignment for unsupervised domain adaptation
A. Kumar, P. Sattigeri, K. Wadhawan, L. Karlinsky, R. S. Feris, W. T. Freeman, and G. Wornell · 2018
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Explicit inductive bias for transfer learning with convolutional networks
X. Li, Y. Grandvalet, and F. Davoine · 2018
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Dynamic deep neural networks: Optimizing accuracy-efficiency trade-offs by selective execution
L. Liu and J. Deng · 2018
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Piggyback: Adding multiple tasks to a single, fixed network by learning to mask
A. Mallya and S. Lazebnik · 2018
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Efficient parametrization of multi-domain deep neural networks
S.-A. Rebuffi, H. Bilen, and A. Vedaldi · 2018
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Convolutional networks with adaptive inference graphs
A. Veit and S. Belongie · 2018
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Blockdrop: Dynamic inference paths in residual networks
Z. Wu, T. Nagarajan, A. Kumar, S. Rennie, L. S. Davis, K. Grauman, and R. Feris · 2018
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Glomo: Unsupervisedly learned relational graphs as transferable representations
Z. Yang, B. Dhingra, K. He, W. W. Cohen, R. Salakhutdinov, Y. LeCun, et al · 2018
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