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Transfer of pre-trained representations can improve sample efficiency and reduce computational requirements for new tasks.
Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
K. Fukushima · 1980
Earlier work this paper cites.
Backpropagation applied to handwritten zip code recognition
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel · 1989
Earlier work this paper cites.
An introduction to kernel and nearest-neighbor nonparametric regression
N. S. Altman · 1992
Earlier work this paper cites.
Learning piecewise control strategies in a modular neural network architecture
R. A. Jacobs and M. I. Jordan · 1993
Earlier work this paper cites.
Multitask learning
R. Caruana · 1997
Earlier work this paper cites.
Learning multiple tasks with kernel methods
T. Evgeniou, C. A. Micchelli, and M. Pontil · 2005
Earlier work this paper cites.
Multi-task feature learning
A. Argyriou, T. Evgeniou, and M. Pontil · 2007
Earlier work this paper cites.
Analysis of representations for domain adaptation
S. Ben-David, J. Blitzer, K. Crammer, and F. Pereira · 2007
Earlier work this paper cites.
Boosting for transfer learning
W. Dai, Q. Yang, G.-R. Xue, and Y. Yu · 2007
Earlier work this paper cites.
Learning bounds for domain adaptation
J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. Wortman · 2008
Earlier work this paper cites.
ImageNet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Clustered multi-task learning: A convex formulation
L. Jacob, J.-P. Vert, and F. R. Bach · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
Earlier work this paper cites.
Taking advantage of sparsity in multi-task learning
K. Lounici, M. Pontil, A. B. Tsybakov, and S. Van De Geer · 2009
Earlier work this paper cites.
Domain adaptation: Learning bounds and algorithms
Y. Mansour, M. Mohri, and A. Rostamizadeh · 2009
Earlier work this paper cites.
Domain adaptation with multiple sources
Y. Mansour, M. Mohri, and A. Rostamizadeh · 2009
Earlier work this paper cites.
A survey on transfer learning
S. J. Pan and Q. Yang · 2009
Earlier work this paper cites.
A theory of learning from different domains
S. Ben-David, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. W. Vaughan · 2010
Earlier work this paper cites.
Domain adaptation via transfer component analysis
S. J. Pan, I. W. Tsang, J. T. Kwok, and Q. Yang · 2010
Earlier work this paper cites.
Boosting for regression transfer
D. Pardoe and P. Stone · 2010
Earlier work this paper cites.
Deep sparse rectifier neural networks
X. Glorot, A. Bordes, and Y. Bengio · 2011
Earlier work this paper cites.
Bi-weighting domain adaptation for cross-language text classification
C. Wan, R. Pan, and J. Li · 2011
Earlier work this paper cites.
Tree-guided group lasso for multi-response regression with structured sparsity, with an application to eqtl mapping
S. Kim, E. P. Xing, et al · 2012
Earlier work this paper cites.
Cats and dogs
O. M. Parkhi, A. Vedaldi, A. Zisserman, and C. V. Jawahar · 2012
Earlier work this paper cites.
Learning factored representations in a deep mixture of experts
D. Eigen, M. Ranzato, and I. Sutskever · 2013
Cited alongside, same era.
3D object representations for fine-grained categorization
J. Krause, M. Stark, J. Deng, and L. Fei-Fei · 2013
Cited alongside, same era.
Stability and hypothesis transfer learning
I. Kuzborskij and F. Orabona · 2013
Cited alongside, same era.
Fine-grained visual classification of aircraft
S. Maji, J. Kannala, E. Rahtu, M. Blaschko, and A. Vedaldi · 2013
Cited alongside, same era.
Birdsnap: Large-scale fine-grained visual categorization of birds
T. Berg, J. Liu, S. W. Lee, M. L. Alexander, D. W. Jacobs, and P. N. Belhumeur · 2014
Cited alongside, same era.
Deep transfer learning with joint adaptation networks
M. Long, H. Zhu, J. Wang, and M. I. Jordan · 2017
Later among the works it cites.
Learning multiple visual domains with residual adapters
S.-A. Rebuffi, H. Bilen, and A. Vedaldi · 2017
Later among the works it cites.
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
Later among the works it cites.
Revisiting unreasonable effectiveness of data in deep learning era
C. Sun, A. Shrivastava, S. Singh, and A. Gupta · 2017
Later among the works it cites.
A unified framework for metric transfer learning
Y. Xu, S. J. Pan, H. Xiong, Q. Wu, R. Luo, H. Min, and H. Song · 2017
Later among the works it cites.
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L. Bossard, M. Guillaumin, and L. Van Gool · 2014
Cited alongside, same era.
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.
Learning and transferring mid-level image representations using convolutional neural networks
M. Oquab, L. Bottou, I. Laptev, and J. Sivic · 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.
Deep domain confusion: Maximizing for domain invariance
E. Tzeng, J. Hoffman, N. Zhang, K. Saenko, and T. Darrell · 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.
Facial landmark detection by deep multi-task learning
Z. Zhang, P. Luo, C. C. Loy, and X. Tang · 2014
Cited alongside, same era.
J. Ngiam, D. Peng, V. Vasudevan, S. Kornblith, Q. V. Le, and R. Pang · 2018
Later among the works it cites.
Efficient parametrization of multi-domain deep neural networks
S.-A. Rebuffi, H. Bilen, and A. Vedaldi · 2018
Later among the works it cites.
Incremental learning through deep adaptation
A. Rosenfeld and J. K. Tsotsos · 2018
Later among the works it cites.
A survey on deep transfer learning
C. Tan, F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu · 2018
Later among the works it cites.
Theoretical guarantees of transfer learning
Z. Wang · 2018
Later among the works it cites.
Group normalization
Y. Wu and K. He · 2018
Later among the works it cites.
A closer look at few-shot classification
W.-Y. Chen, Y.-C. Liu, Z. Kira, Y.-C. F. Wang, and J.-B. Huang · 2019
Later among the works it cites.
Hyperbolic discounting and learning over multiple horizons
W. Fedus, C. Gelada, Y. Bengio, M. G. Bellemare, and H. Larochelle · 2019
Later among the works it cites.
Parameter-efficient transfer learning for NLP
N. Houlsby, A. Giurgiu, S. Jastrzebski, B. Morrone, Q. De Laroussilhe, A. Gesmundo, M. Attariyan, and S. Gelly · 2019
Later among the works it cites.
Big transfer (BiT): General visual representation learning
A. Kolesnikov, L. Beyer, X. Zhai, J. Puigcerver, J. Yung, S. Gelly, and N. Houlsby · 2019
Later among the works it cites.
Regularized evolution for image classifier architecture search
E. Real, A. Aggarwal, Y. Huang, and Q. V. Le · 2019
Later among the works it cites.
Self-training with noisy student improves imagenet classification
Q. Xie, E. Hovy, M.-T. Luong, and Q. V. Le · 2019
Later among the works it cites.
Billion-scale semi-supervised learning for image classification
I. Z. Yalniz, H. Jégou, K. Chen, M. Paluri, and D. Mahajan · 2019
Later among the works it cites.
The visual task adaptation benchmark
X. Zhai, J. Puigcerver, A. Kolesnikov, P. Ruyssen, C. Riquelme, M. Lucic, J. Djolonga, A. S. Pinto, M. Neumann, A. Dosovitskiy, et al · 2019
Later among the works it cites.
Selecting relevant features from a universal representation for few-shot classification
N. Dvornik, C. Schmid, and J. Mairal · 2020
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PyTorch Hub
PyTorch · 2020
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TensorFlow Hub
TensorFlow · 2020
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Neural data server: A large-scale search engine for transfer learning data, 2020
X. Yan, D. Acuna, and S. Fidler · 2020
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