Fetching the paper…
Reading the bibliography…
When labeled data is scarce for a specific target task, transfer learning often offers an effective solution by utilizing data from a related source task.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
To transfer or not to transfer
M. T. Rosenstein, Z. Marx, L. P. Kaelbling, and T. G. Dietterich · 2005
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.
Correcting sample selection bias by unlabeled data
J. Huang, A. Gretton, K. M. Borgwardt, B. Schölkopf, and A. J. Smola · 2007
Earlier work this paper cites.
Learning bounds for importance weighting
C. Cortes, Y. Mansour, and M. Mohri · 2010
Earlier work this paper cites.
A survey on transfer learning
S. J. Pan and Q. Yang · 2010
Earlier work this paper cites.
Adapting visual category models to new domains
K. Saenko, B. Kulis, M. Fritz, and T. Darrell · 2010
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng · 2011
Earlier work this paper cites.
Domain adaptation via transfer component analysis
S. J. Pan, I. W. Tsang, J. T. Kwok, and Q. Yang · 2011
Earlier work this paper cites.
Feature selection for transfer learning
S. Uguroglu and J. Carbonell · 2011
Earlier work this paper cites.
Exploiting web images for event recognition in consumer videos: A multiple source domain adaptation approach
L. Duan, D. Xu, and S.-F. Chang · 2012
Earlier work this paper cites.
Analysis of kernel mean matching under covariate shift
Y.-L. Yu and C. Szepesvári · 2012
Earlier work this paper cites.
A theory of transfer learning with applications to active learning
L. Yang, S. Hanneke, and J. Carbonell · 2013
Earlier work this paper cites.
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.
On handling negative transfer and imbalanced distributions in multiple source transfer learning
L. Ge, J. Gao, H. Ngo, K. Li, and A. Zhang · 2014
Cited alongside, same era.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Cited alongside, same era.
Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 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.
Unsupervised domain adaptation by backpropagation
Y. Ganin and V. Lempitsky · 2015
A survey of transfer learning
K. Weiss, T. M. Khoshgoftaar, and D. Wang · 2016
Later among the works it cites.
Completely heterogeneous transfer learning with attention-what and what not to transfer
S. Moon and J. Carbonell · 2017
Later among the works it cites.
Visda: The visual domain adaptation challenge, 2017
X. Peng, B. Usman, N. Kaushik, J. Hoffman, D. Wang, and K. Saenko · 2017
Later among the works it cites.
Youtube-boundingboxes: A large high-precision human-annotated data set for object detection in video
E. Real, J. Shlens, S. Mazzocchi, X. Pan, and V. Vanhoucke · 2017
Later among the works it cites.
Adversarial discriminative domain adaptation
E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell · 2017
Later among the works it cites.
Deep hashing network for unsupervised domain adaptation
H. Venkateswara, J. Eusebio, S. Chakraborty, and S. Panchanathan · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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.
Simultaneous deep transfer across domains and tasks
E. Tzeng, J. Hoffman, T. Darrell, and K. Saenko · 2015
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.
Domain adaptation with conditional transferable components
M. Gong, K. Zhang, T. Liu, D. Tao, C. Glymour, and B. Schölkopf · 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.
Deep coral: Correlation alignment for deep domain adaptation
B. Sun and K. Saenko · 2016
Cited alongside, same era.
Later among the works it cites.
Why do deep convolutional networks generalize so poorly to small image transformations?
A. Azulay and Y. Weiss · 2018
Closest in time.
Partial transfer learning with selective adversarial networks
Z. Cao, M. Long, J. Wang, and M. I. Jordan · 2018
Closest in time.
Partial adversarial domain adaptation
Z. Cao, L. Ma, M. Long, and J. Wang · 2018
Closest in time.
Multi-adversarial domain adaptation
Z. Pei, Z. Cao, M. Long, and J. Wang · 2018
Closest in time.
Generate to adapt: Aligning domains using generative adversarial networks
S. Sankaranarayanan, Y. Balaji, C. D. Castillo, and R. Chellappa · 2018
Closest in time.
Towards more reliable transfer learning
Z. Wang and J. Carbonell · 2018
Closest in time.
Taskonomy: Disentangling task transfer learning
A. R. Zamir, A. Sax, W. Shen, L. Guibas, J. Malik, and S. Savarese · 2018
Closest in time.