Fetching the paper…
Reading the bibliography…
Recent works showed that Generative Adversarial Networks (GANs) can be successfully applied in unsupervised domain adaptation, where, given a labeled source dataset and an unlabeled target dataset, the goal is to train powerful classifiers for the target samples.
Parallel distributed processing: Explorations in the microstructure of cognition, vol. 1
D. E. Rumelhart, G. E. Hinton, and R. J. Williams · 1986
Earlier work this paper cites.
Advances in neural information processing systems 1
J. S. Denker, W. R. Gardner, H. P. Graf, D. Henderson, R. E. Howard, W. Hubbard, L. D. Jackel, H. S. Baird, and I. Guyon · 1989
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
Visualizing high-dimensional data using t-sne
L. van der Maaten and G. Hinton · 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.
Rectified linear units improve restricted boltzmann machines
V. Nair and G. E. Hinton · 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.
Unbiased look at dataset bias
A. Torralba and A. A. Efros · 2011
Earlier work this paper cites.
A kernel two-sample test
A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola · 2012
Earlier work this paper cites.
Indoor segmentation and support infer- ence from rgbd images
N. Silberman, D. Hoiem, P. Kohli, , and R. Fergus · 2012
Earlier work this paper cites.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Earlier work this paper cites.
Learning rich features from rgb-d images for object detection and segmentation
S. Gupta, R. Girshick, P. A. aez, and J. Malik · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Earlier work this paper cites.
Conditional generative adversarial nets
M. Mirza and S. Osindero · 2014
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Cited alongside, same era.
Dropout: A simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 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.
Unsupervised domain adaptation by backpropagation
Y. Ganin and V. S. Lempitsky · 2015
Cited alongside, same era.
Coupled generative adversarial networks
M.-Y. Liu and O. Tuzel · 2016
Later among the works it cites.
Multi-class generative adversarial networks with the L2 loss function
X. Mao, Q. Li, H. Xie, R. Y. K. Lau, and Z. Wang · 2016
Later among the works it cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2016
Later among the works it cites.
Learning what and where to draw
S. E. Reed, Z. Akata, S. Mohan, S. Tenka, B. Schiele, and H. Lee · 2016
Later among the works it cites.
Generating videos with scene dynamics
C. Vondrick, H. Pirsiavash, and A. Torralba · 2016
Later among the works it cites.
Unsupervised pixel-level domain adaptation with generative adversarial networks
K. Bousmalis, N. Silberman, D. Dohan, D. Erhan, and D. Krishnan · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
S. Ioffe and C. Szegedy · 2015
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.
Domain separation networks
K. Bousmalis, G. Trigeorgis, N. Silberman, D. Krishnan, and D. Erhan · 2016
Cited alongside, same era.
Infogan: Interpretable representation learning by information maximizing generative adversarial nets
X. Chen, X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel · 2016
Cited alongside, same era.
J. Donahue, P. Krähenbühl, and T. Darrell · 2016
Cited alongside, same era.
Adversarially learned inference
V. Dumoulin, I. Belghazi, B. Poole, A. Lamb, M. Arjovsky, O. Mastropietro, and A. Courville · 2016
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.
Closest in time.
Associative domain adaptation
P. Haeusser, T. Frerix, A. Mordvintsev, and D. Cremers · 2017
Closest in time.
Unsupervised image-to-image translation networks
M. Liu, T. Breuel, and J. Kautz · 2017
Closest in time.
On the expressive power of deep neural networks
M. Raghu, B. Poole, J. Kleinberg, S. Ganguli, and J. Sohl-Dickstein · 2017
Closest in time.
Asymmetric tri-training for unsupervised domain adaptation
K. Saito, Y. Ushiku, and T. Harada · 2017
Closest in time.
Unsupervised cross-domain image generation
Y. Taigman, A. Polyak, and L. Wolf · 2017
Closest in time.
Adversarial discriminative domain adaptation
E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell · 2017
Closest in time.