Fetching the paper…
Reading the bibliography…
In this paper, we propose an approach to the domain adaptation, dubbed Second- or Higher-order Transfer of Knowledge (So-HoT), based on the mixture of alignments of second- or higher-order scatter statistics between the source and target domains.
The influence of improvement in one mental function upon the efficiency of other functions
R. S. Woodworth and E. L. Thorndike · 1901
Earlier work this paper cites.
Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
Kunihiko Fukushima · 1980
Earlier work this paper cites.
Neural networks and physical systems with emergent collective computational abilities
J. J. Hopfield · 1982
Earlier work this paper cites.
Making a low-dimensional representation suitable for diverse tasks
Nathan Intrator and Shimon Edelman · 1996
Earlier work this paper cites.
Is learning the n-th thing any easier than learning the first?
Sebastian Thrun · 1996
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
A multilinear singular value decomposition
L. De Lathauwer, B. De Moor, and J. Vandewalle · 2000
Earlier work this paper cites.
Multilinear analysis of image ensembles: Tensorfaces
M. A. Vasilescu and D. Terzopoulos · 2002
Earlier work this paper cites.
Tensortextures: multilinear image-based rendering
M. A. Vasilescu and D. Terzopoulos · 2004
Earlier work this paper cites.
Non-negative tensor factorization with applications to statistics and computer vision
A. Shashua and T. Hazan · 2005
Earlier work this paper cites.
One-shot learning of object categories
R.; Perona L. Fei-Fei; Fergus · 2006
Earlier work this paper cites.
The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2007
Earlier work this paper cites.
Caltech-256 object category dataset
G. Griffin, A. Holub, and P. Perona · 2007
Earlier work this paper cites.
Tensor canonical correlation analysis for action classification
Tae-Kyun Kim, Kwan-Yee Kenneth Wong, and R. Cipolla · 2007
Earlier work this paper cites.
Spring research presentation: A theoretical foundation for inductive transfer
J. West, D. Venture, and S. Warnick · 2007
Earlier work this paper cites.
Learning from multiple sources
K. Crammer, M. Kearns, and J. Wortman · 2008
Cited alongside, same era.
A new learning paradigm: Learning using privileged information
Vladimir Vapnik and Akshay Vashist · 2009
Cited alongside, same era.
Frustratingly easy semi-supervised domain adaptation
Hal Daumé, III, Abhishek Kumar, and Avishek Saha · 2010
Cited alongside, same era.
Adapting visual category models to new domains
Kate Saenko, Brian Kulis, Mario Fritz, and Trevor Darrell · 2010
Cited alongside, same era.
Safety in numbers: Learning categories from few examples with multi model knowledge transfer
Tatiana Tommasi, Francesco Orabona, and Barbara Caputo · 2010
Cited alongside, same era.
A large-scale hierarchical multi-view rgb-d object dataset
Kevin Lai, Liefeng Bo, Xiaofeng Ren, and Dieter Fox · 2011
Cited alongside, same era.
Heterogeneous domain adaptation and classification by exploiting the correlation subspace
Yi-Ren Yeh, Chun-Hao Huang, and Yu-Chiang Frank Wang · 2014
Later among the works it cites.
Learning transferable features with deep adaptation networks
Mingsheng Long, Yue Cao, Jianmin Wang, and Michael Jordan · 2015
Later among the works it cites.
ImageNet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
Later among the works it cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
Later among the works it cites.
Return of frustratingly easy domain adaptation
Baochen Sun, Jiashi Feng, and Kate Saenko · 2015
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A survey of multilinear subspace learning for tensor data
H. Lu, K. N. Plataniotis, and A. N. Venetsanopoulos · 2011
Cited alongside, same era.
How do humans sketch objects?
Mathias Eitz, James Hays, and Marc Alexa · 2012
Cited alongside, same era.
ImageNet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
Cited alongside, same era.
Dlid: Deep learning for domain adaptation by interpolating between domains
Sumit Chopra, Suhrid Balakrishnan, and Raghuraman Gopalan · 2013
Cited alongside, same era.
Recognizing rgb images by learning from rgb-d data
Lin Chen, Wen Li, and Dong Xu · 2014
Cited alongside, same era.
Decaf: A deep convolutional activation feature for generic visual recognition
Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, and Trevor Darrell · 2014
Cited alongside, same era.
Simultaneous deep transfer across domains and tasks
E. Tzeng, J. Hoffman, T. Darrell, and K. Saenko · 2015
Later among the works it cites.
Learning to learn: Knowledge consolidation and transfer in inductive systems
Jonathan Baxter, Rich Caruana, Tom Mitchell, Lorien Y. Pratt, Daniel L. Silver, and Sebastian Thrun · 2016
Closest in time.
Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
Closest in time.
Generalized backpropagation, étude de cas: Orthogonality
Mehrtash Harandi and Basura Fernando · 2016
Closest in time.
Sparse coding for third-order super-symmetric tensor descriptors with application to texture recognition
P. Koniusz and A. Cherian · 2016
Closest in time.
When naïve bayes nearest neighbors meet convolutional neural networks
Ilja Kuzborskij, Fabio Maria Carlucci, and Barbara Caputo · 2016
Closest in time.
Task-cv: Transferring and adapting source knowledge in computer vision
W. Li, T. Tommasi, F. Orabona, D. Vázquez, M. López, J. Xu, and H. Larochelle · 2016
Closest in time.
Information bottleneck domain adaptation with privileged information for visual recognition
Saeid Motiian and Gianfranco Doretto · 2016
Closest in time.
Learning the roots of visual domain shift
Tatiana Tommasi, Martina Lanzi, Paolo Russo, and Barbara Caputo · 2016
Closest in time.
Learning to learn: Model regression networks for easy small sample learning
Yu-Xiong Wang and Martial Hebert · 2016
Closest in time.