Fetching the paper…
Reading the bibliography…
Transfer learning aims to improve learning in target domain by borrowing knowledge from a related but different source domain.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Rademacher and gaussian complexities: Risk bounds and structural results
Peter L Bartlett and Shahar Mendelson · 2002
Earlier work this paper cites.
Correcting sample selection bias by unlabeled data
Jiayuan Huang, Arthur Gretton, Karsten M Borgwardt, Bernhard Schölkopf, and Alex J Smola · 2007
Earlier work this paper cites.
Noise tolerant variants of the perceptron algorithm
Roni Khardon and Gabriel Wachman · 2007
Earlier work this paper cites.
Random classification noise defeats all convex potential boosters
Philip M Long and Rocco A Servedio · 2008
Earlier work this paper cites.
Composite binary losses
Mark D Reid and Robert C Williamson · 2010
Earlier work this paper cites.
Domain adaptation for object recognition: An unsupervised approach
Raghuraman Gopalan, Ruonan Li, and Rama Chellappa · 2011
Earlier work this paper cites.
Domain adaptation via transfer component analysis
Sinno Jialin Pan, Ivor W Tsang, James T Kwok, and Qiang Yang · 2011
Earlier work this paper cites.
Transportability of causal and statistical relations: A formal approach
Judea Pearl and Elias Bareinboim · 2011
Earlier work this paper cites.
Unbiased look at dataset bias
Antonio Torralba and Alexei A Efros · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Foundations of machine learning
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar · 2012
Earlier work this paper cites.
On causal and anticausal learning
B. Schölkopf, D. Janzing, J. Peters, E. Sgouritsa, K. Zhang, and J. Mooij · 2012
Earlier work this paper cites.
On causal and anticausal learning
Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, and Joris Mooij · 2012
Earlier work this paper cites.
Unsupervised domain adaptation by domain invariant projection
Mahsa Baktashmotlagh, Mehrtash T Harandi, Brian C Lovell, and Mathieu Salzmann · 2013
Cited alongside, same era.
Concentration inequalities: A nonasymptotic theory of independence
Stéphane Boucheron, Gábor Lugosi, and Pascal Massart · 2013
Cited alongside, same era.
Probability in Banach Spaces: Isoperimetry and processes
Michel Ledoux and Michel Talagrand · 2013
Cited alongside, same era.
Learning with noisy labels
Nagarajan Natarajan, Inderjit S Dhillon, Pradeep K Ravikumar, and Ambuj Tewari · 2013
Cited alongside, same era.
Covariate shift in hilbert space: A solution via sorrogate kernels
Kai Zhang, Vincent Zheng, Qiaojun Wang, James Kwok, Qiang Yang, and Ivan Marsic · 2013
Cited alongside, same era.
Domain adaptation under target and conditional shift
Kun Zhang, Bernhard Schölkopf, Krikamol Muandet, and Zhikun Wang · 2013
Auxiliary image regularization for deep CNNs with noisy labels
Samaneh Azadi, Jiashi Feng, Stefanie Jegelka, and Trevor Darrell · 2016
Later among the works it cites.
Domain adaptation with conditional transferable components
Mingming Gong, Kun Zhang, Tongliang Liu, Dacheng Tao, Clark Glymour, and Bernhard Schölkopf · 2016
Later among the works it cites.
FCNs in the wild: Pixel-level adversarial and constraint-based adaptation
Judy Hoffman, Dequan Wang, Fisher Yu, and Trevor Darrell · 2016
Later among the works it cites.
Classification with noisy labels by importance reweighting
Tongliang Liu and Dacheng Tao · 2016
Later among the works it cites.
Mixture Proportion Estimation via kernel embeddings of distributions
Harish Ramaswamy, Clayton Scott, and Ambuj Tewari · 2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Maximum Mean Discrepancy for class ratio estimation: Convergence bounds and kernel selection
Arun Iyer, J Saketha Nath, and Sunita Sarawagi · 2014
Cited alongside, same era.
Caffe: Convolutional architecture for fast feature embedding
Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell · 2014
Cited alongside, same era.
Training convolutional networks with noisy labels
Sainbayar Sukhbaatar, Joan Bruna, Manohar Paluri, Lubomir Bourdev, and Rob Fergus · 2014
Cited alongside, same era.
Active transfer learning under model shift
Xuezhi Wang, Tzu-Kuo Huang, and Jeff G. Schneider · 2014
Cited alongside, same era.
Learning transferable features with deep adaptation networks
Mingsheng Long, Yue Cao, Jianmin Wang, and Michael I Jordan · 2015
Cited alongside, same era.
A rate of convergence for Mixture Proportion Estimation, with application to learning from noisy labels
Clayton Scott · 2015
Cited alongside, same era.
José A Sáez, Bartosz Krawczyk, and Michał Woźniak · 2016
Later among the works it cites.
The effectiveness of transfer learning in electronic health records data
Sebastien Dubois, Nathanael Romano, Kenneth Jung, Nigam Shah, and David C Kale · 2017
Closest in time.
Deep transfer learning with joint adaptation networks
Mingsheng Long, Jianmin Wang, and Michael I Jordan · 2017
Closest in time.
Making deep neural networks robust to label noise: A loss correction approach
Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, and Lizhen Qu · 2017
Closest in time.
Visda: The visual domain adaptation challenge
Xingchao Peng, Ben Usman, Neela Kaushik, Judy Hoffman, Dequan Wang, and Kate Saenko · 2017
Closest in time.
Variational recurrent adversarial deep domain adaptation
Sanjay Purushotham, Wilka Carvalho, Tanachat Nilanon, and Yan Liu · 2017
Closest in time.
Cleannet: Transfer learning for scalable image classifier training with label noise
Kuang-Huei Lee, Xiaodong He, Lei Zhang, and Linjun Yang · 2018
Closest in time.
An efficient and provable approach for mixture proportion estimation using linear independence assumption
Xiyu Yu, Tongliang Liu, Mingming Gong, Kayhan Batmanghelich, and Dacheng Tao · 2018
Closest in time.