Fetching the paper…
Reading the bibliography…
Domain adaptation addresses the problem created when training data is generated by a so-called source distribution, but test data is generated by a significantly different target distribution.
Combining labeled and unlabeled data with co-training
Blum, A. and Mitchell, T · 1998
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Yann, Bottou, Léon, Bengio, Yoshua, and Haffner, Patrick · 1998
Earlier work this paper cites.
Reinforcement learning: An introduction , volume 1
Sutton, R.S. and Barto, A.G · 1998
Earlier work this paper cites.
Biographies, Bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification
Blitzer, J., Dredze, M., and Pereira, F · 2007
Earlier work this paper cites.
Covariate shift adaptation by importance weighted cross validation
Sugiyama, M., Krauledat, M., and MÞller, K.R · 2007
Earlier work this paper cites.
Visualizing data using t-sne
Maaten, Laurens van der and Hinton, Geoffrey · 2008
Earlier work this paper cites.
Covariate shift by kernel mean matching
Gretton, A., Smola, A., Huang, J., Schmittfull, M., Borgwardt, K., and Schölkopf, B · 2009
Earlier work this paper cites.
Adapting visual category models to new domains
Saenko, K., Kulis, B., Fritz, M., and Darrell, T · 2010
Earlier work this paper cites.
Torch7: A matlab-like environment for machine learning
Collobert, R., Kavukcuoglu, K., and Farabet, C · 2011
Cited alongside, same era.
Domain adaptation for large-scale sentiment classification: A deep learning approach
Glorot, X., Bordes, A., and Bengio, Y · 2011
Cited alongside, same era.
A parametric empirical bayesian framework for the eeg/meg inverse problem: generative models for multi-subject and multi-modal integration
Henson, R.N., Wakeman, D.G., Litvak, V., and Friston, K.J · 2011
Cited alongside, same era.
Geodesic flow kernel for unsupervised domain adaptation
Gong, B., Shi, Y., Sha, F., and Grauman, K · 2012
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G.E · 2012
Cited alongside, same era.
Regularized hyperalignment of multi-set fmri data
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
Later among the works it cites.
Meg decoding across subjects
Olivetti, E., Mostafa, S.K., and Avesani, P · 2014
Later among the works it cites.
Domain generalization for object recognition with multi-task autoencoders
Ghifary, M., Bastiaan, K.W., Zhang, M., and Balduzzi, D · 2015
Later among the works it cites.
Learning representations for counterfactual inference
Johansson, F.D., Shalit, U., and Sontag, D · 2015
Later among the works it cites.
Adam: A method for stochastic optimization
Kingma, D. and Ba, J · 2015
Later among the works it cites.
Domain-adversarial training of neural networks
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., and Lempitsky, V · 2016
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Xu, H., Lorbert, A., Ramadge, P.J., Guntupalli, J.S., and Haxby, J.V · 2012
Cited alongside, same era.
Classification of covariance matrices using a riemannian-based kernel for bci applications
Barachant, A., Bonnet, S., Congedo, M., and Jutten, C · 2013
Cited alongside, same era.
Unsupervised visual domain adaptation using subspace alignment
Fernando, B., Habrard, A., Sebban, M., and Tuytelaars, T · 2013
Cited alongside, same era.
Towards principled methods for training generative adversarial networks
Arjovsky, M. and Bottou, L · 2017
Closest in time.