Built on

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” in Proceedings of the IEEE , 1998, pp. 2278–2324
1998
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in Neural Information Processing Systems 27 , Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger, Eds. Curran Associates, Inc., 2014, pp. 2672–2680. [Online]. Available: http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf
2014
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” International Conference on Learning Representations (ICLR) , 2014. [Online]. Available: http://arxiv.org/abs/1312.6114
Original
2014
Earlier work this paper cites.
D. J. Rezende, S. Mohamed, and D. Wierstra, “Stochastic backpropagation and approximate inference in deep generative models,” Proceedings of the 31st International Conference on Machine Learning (ICML) , vol. 32, pp. 1278–1286, 2014
2014
Earlier work this paper cites.
D. P. Kingma, D. J. Rezende, S. Mohamed, and M. Welling, “Semi-supervised learning with deep generative models,” Proceedings of Neural Information Processing Systems (NIPS) , 2014. [Online]. Available: http://arxiv.org/abs/1406.5298
Original
2014
Earlier work this paper cites.
M. Mirza and S. Osindero, “Conditional Generative Adversarial Nets,” CoRR , vol. abs/1411.1784, 2014. [Online]. Available: http://arxiv.org/abs/1411.1784
Original
2014
Earlier work this paper cites.
J. Gauthier, “Conditional generative adversarial nets for convolutional face generation,” Class project for Stanford CS231N: Convolutional Neural Networks for Visual Recognition, Winter semester 2014 , 2014. [Online]. Available: http://cs231n.stanford.edu/reports/jgauthie_final_report.pdf
2014
Earlier work this paper cites.

Similar

D. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” International Conference for Learning Representations (ICLR) , 2014. [Online]. Available: http://www.arxiv.org/pdf/1412.6980.pdf
Original
2014
Cited alongside, same era.
K. Gregor, I. Danihelka, A. Graves, D. Jimenez Rezende, and D. Wierstra, “DRAW: A Recurrent Neural Network For Image Generation,” International Conference on Machine Learning (ICML) , pp. 1462–1471, 2015
2015
Cited alongside, same era.
Z. Liu, P. Luo, X. Wang, and X. Tang, “Deep learning face attributes in the wild,” in Proceedings of International Conference on Computer Vision (ICCV) , December 2015
2015
Cited alongside, same era.
A. B. L. Larsen, S. K. Sønderby, and O. Winther, “Autoencoding beyond pixels using a learned similarity metric,” Proceedings of the 33rd International Conference on Machine Learning (ICML) , pp. 1558–1566, 2015. [Online]. Available: http://arxiv.org/abs/1512.09300
Original
2015
Cited alongside, same era.

Then

X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel, “InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets,” Neural Information Processing Systems (NIPS) , 2016. [Online]. Available: http://arxiv.org/abs/1606.03657
Original
2016
Closest in time.
S. Reed, Z. Akata, S. Mohan, S. Tenka, B. Schiele, and H. Lee, “Learning what and where to draw,” in Advances in Neural Information Processing Systems (NIPS) , 2016
2016
Closest in time.
V. Dumoulin, I. Belghazi, B. Poole, A. Lamb, M. Arjovsky, O. Mastropietro, and A. Courville, “Adversarially Learned Inference,” Arxiv , 2016. [Online]. Available: http://arxiv.org/abs/1606.00704
Original
2016
Closest in time.
J. Donahue, P. Krähenbühl, and T. Darrell, “Adversarial feature learning,” CoRR , vol. abs/1605.09782, 2016. [Online]. Available: http://arxiv.org/abs/1605.09782
Original
2016
Closest in time.

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2016

Invertible Conditional GANs for image editing

Perarnau, Guim, van de Weijer, Joost, Raducanu, Bogdan et al.

Understand

Generative Adversarial Networks (GANs) have recently demonstrated to successfully approximate complex data distributions.

A relevant extension of this model is conditional GANs (cGANs), where the introduction of external information allows to determine specific representations of the generated images.
In this work, we evaluate encoders to inverse the mapping of a cGAN, i.e., mapping a real image into a latent space and a conditional representation.
This allows, for example, to reconstruct and modify real images of faces conditioning on arbitrary attributes.

Built on

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” in Proceedings of the IEEE , 1998, pp. 2278–2324
1998
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in Neural Information Processing Systems 27 , Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger, Eds. Curran Associates, Inc., 2014, pp. 2672–2680. [Online]. Available: http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf
2014
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” International Conference on Learning Representations (ICLR) , 2014. [Online]. Available: http://arxiv.org/abs/1312.6114
Original
2014
Earlier work this paper cites.
D. J. Rezende, S. Mohamed, and D. Wierstra, “Stochastic backpropagation and approximate inference in deep generative models,” Proceedings of the 31st International Conference on Machine Learning (ICML) , vol. 32, pp. 1278–1286, 2014
2014
Earlier work this paper cites.
D. P. Kingma, D. J. Rezende, S. Mohamed, and M. Welling, “Semi-supervised learning with deep generative models,” Proceedings of Neural Information Processing Systems (NIPS) , 2014. [Online]. Available: http://arxiv.org/abs/1406.5298
Original
2014
Earlier work this paper cites.
M. Mirza and S. Osindero, “Conditional Generative Adversarial Nets,” CoRR , vol. abs/1411.1784, 2014. [Online]. Available: http://arxiv.org/abs/1411.1784
Original
2014
Earlier work this paper cites.
J. Gauthier, “Conditional generative adversarial nets for convolutional face generation,” Class project for Stanford CS231N: Convolutional Neural Networks for Visual Recognition, Winter semester 2014 , 2014. [Online]. Available: http://cs231n.stanford.edu/reports/jgauthie_final_report.pdf
2014
Earlier work this paper cites.

Similar

D. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” International Conference for Learning Representations (ICLR) , 2014. [Online]. Available: http://www.arxiv.org/pdf/1412.6980.pdf
Original
2014
Cited alongside, same era.
K. Gregor, I. Danihelka, A. Graves, D. Jimenez Rezende, and D. Wierstra, “DRAW: A Recurrent Neural Network For Image Generation,” International Conference on Machine Learning (ICML) , pp. 1462–1471, 2015
2015
Cited alongside, same era.
Z. Liu, P. Luo, X. Wang, and X. Tang, “Deep learning face attributes in the wild,” in Proceedings of International Conference on Computer Vision (ICCV) , December 2015
2015
Cited alongside, same era.
A. B. L. Larsen, S. K. Sønderby, and O. Winther, “Autoencoding beyond pixels using a learned similarity metric,” Proceedings of the 33rd International Conference on Machine Learning (ICML) , pp. 1558–1566, 2015. [Online]. Available: http://arxiv.org/abs/1512.09300
Original
2015
Cited alongside, same era.

Then

X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel, “InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets,” Neural Information Processing Systems (NIPS) , 2016. [Online]. Available: http://arxiv.org/abs/1606.03657
Original
2016
Closest in time.
S. Reed, Z. Akata, S. Mohan, S. Tenka, B. Schiele, and H. Lee, “Learning what and where to draw,” in Advances in Neural Information Processing Systems (NIPS) , 2016
2016
Closest in time.
V. Dumoulin, I. Belghazi, B. Poole, A. Lamb, M. Arjovsky, O. Mastropietro, and A. Courville, “Adversarially Learned Inference,” Arxiv , 2016. [Online]. Available: http://arxiv.org/abs/1606.00704
Original
2016
Closest in time.
J. Donahue, P. Krähenbühl, and T. Darrell, “Adversarial feature learning,” CoRR , vol. abs/1605.09782, 2016. [Online]. Available: http://arxiv.org/abs/1605.09782
Original
2016
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

A. Radford, L. Metz, and S. Chintala, “Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks,” International Conference on Learning Representations (ICLR) , 2016

2016

Cited alongside, same era.

T. Salimans, I. J. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, “Improved techniques for training GANs,” Neural Information Processing Systems (NIPS) , 2016. [Online]. Available: http://arxiv.org/abs/1606.03498

Original

2016

Cited alongside, same era.

A. Makhzani, J. Shlens, N. Jaitly, and I. Goodfellow, “Adversarial autoencoders,” in International Conference on Learning Representations (ICLR) , 2016. [Online]. Available: http://arxiv.org/abs/1511.05644

Original

2016

Closest in time.

S. E. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, and H. Lee, “Generative adversarial text to image synthesis,” International Conference on Machine Learning (ICML) , 2016. [Online]. Available: http://arxiv.org/abs/1605.05396

Original

2016

Closest in time.