Built on

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE conference on computer vision and pattern recognition
Original
1912
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
2014
Earlier work this paper cites.
M. Mirza and S. Osindero, “Conditional generative adversarial nets,” arXiv preprint arXiv:1411.1784
Original
2014
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980
Original
2014
Earlier work this paper cites.
A. Radford, L. Metz, and S. Chintala, “Unsupervised representation learning with deep convolutional generative adversarial networks,” arXiv preprint arXiv:1511.06434
Original
2015
Earlier work this paper cites.
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” arXiv preprint arXiv:1502.03167
Original
2015
Earlier work this paper cites.
Z. Liu, P. Luo, X. Wang, and X. Tang, “Deep learning face attributes in the wild,” in Proceedings of the IEEE international conference on computer vision
2015
Earlier work this paper cites.
M. Li, W. Zuo, and D. Zhang, “Deep identity-aware transfer of facial attributes,” arXiv preprint arXiv:1610.05586
Original
2016
Earlier work this paper cites.

Similar

G. Perarnau, J. Van De Weijer, B. Raducanu, and J. M. Álvarez, “Invertible conditional gans for image editing,” arXiv preprint arXiv:1611.06355
Original
2016
Cited alongside, same era.
W. Shen and R. Liu, “Learning residual images for face attribute manipulation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2017
Cited alongside, same era.
M.-Y. Liu, T. Breuel, and J. Kautz, “Unsupervised image-to-image translation networks,” in Advances in neural information processing systems
2017
Cited alongside, same era.
G. Lample, N. Zeghidour, N. Usunier, A. Bordes, L. Denoyer, and M. Ranzato, “Fader networks: Manipulating images by sliding attributes,” in Advances in Neural Information Processing Systems
2017
Cited alongside, same era.
S. Zhou, T. Xiao, Y. Yang, D. Feng, Q. He, and W. He, “Genegan: Learning object transfiguration and attribute subspace from unpaired data,” arXiv preprint arXiv:1705.04932

Then

I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville, “Improved training of wasserstein gans,” in Advances in neural information processing systems
2017
Later among the works it cites.
T. Karras, T. Aila, S. Laine, and J. Lehtinen, “Progressive growing of gans for improved quality, stability, and variation,” arXiv preprint arXiv:1710.10196
Original
2017
Later among the works it cites.
A. Odena, C. Olah, and J. Shlens, “Conditional image synthesis with auxiliary classifier gans,” in Proceedings of the 34th International Conference on Machine Learning-Volume 70
2017
Later among the works it cites.
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “Gans trained by a two time-scale update rule converge to a local nash equilibrium,” in Advances in Neural Information Processing Systems
2017
Later among the works it cites.

Beyond the bibliography

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

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Reading the bibliography…

2019

MulGAN: Facial Attribute Editing by Exemplar

Guo, Jingtao, Qian, Zhenzhen, Zhou, Zuowei et al.

Understand

Recent studies on face attribute editing by exemplars have achieved promising results due to the increasing power of deep convolutional networks and generative adversarial networks.

These methods encode attribute-related information in images into the predefined region of the latent feature space by employing a pair of images with opposite attributes as input to train model, the face attribute transfer between the input image and the exemplar can be achieved by exchanging their attribute-related latent feature region.
However, they suffer from three limitations: (1) the model must be trained using a pair of images with opposite attributes as input; (2) weak capability of editing multiple attributes by exemplars; (3) poor quality of generating image.
Instead of imposing opposite-attribute constraints on the input image in order to make the attribute information of images be encoded in the predefined region of the latent feature space, in this work we directly apply the attribute labels constraint to the predefined region of the latent feature space.

Built on

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE conference on computer vision and pattern recognition
Original
1912
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
2014
Earlier work this paper cites.
M. Mirza and S. Osindero, “Conditional generative adversarial nets,” arXiv preprint arXiv:1411.1784
Original
2014
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980
Original
2014
Earlier work this paper cites.
A. Radford, L. Metz, and S. Chintala, “Unsupervised representation learning with deep convolutional generative adversarial networks,” arXiv preprint arXiv:1511.06434
Original
2015
Earlier work this paper cites.
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” arXiv preprint arXiv:1502.03167
Original
2015
Earlier work this paper cites.
Z. Liu, P. Luo, X. Wang, and X. Tang, “Deep learning face attributes in the wild,” in Proceedings of the IEEE international conference on computer vision
2015
Earlier work this paper cites.
M. Li, W. Zuo, and D. Zhang, “Deep identity-aware transfer of facial attributes,” arXiv preprint arXiv:1610.05586
Original
2016
Earlier work this paper cites.

Similar

G. Perarnau, J. Van De Weijer, B. Raducanu, and J. M. Álvarez, “Invertible conditional gans for image editing,” arXiv preprint arXiv:1611.06355
Original
2016
Cited alongside, same era.
W. Shen and R. Liu, “Learning residual images for face attribute manipulation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2017
Cited alongside, same era.
M.-Y. Liu, T. Breuel, and J. Kautz, “Unsupervised image-to-image translation networks,” in Advances in neural information processing systems
2017
Cited alongside, same era.
G. Lample, N. Zeghidour, N. Usunier, A. Bordes, L. Denoyer, and M. Ranzato, “Fader networks: Manipulating images by sliding attributes,” in Advances in Neural Information Processing Systems
2017
Cited alongside, same era.
S. Zhou, T. Xiao, Y. Yang, D. Feng, Q. He, and W. He, “Genegan: Learning object transfiguration and attribute subspace from unpaired data,” arXiv preprint arXiv:1705.04932

Then

I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville, “Improved training of wasserstein gans,” in Advances in neural information processing systems
2017
Later among the works it cites.
T. Karras, T. Aila, S. Laine, and J. Lehtinen, “Progressive growing of gans for improved quality, stability, and variation,” arXiv preprint arXiv:1710.10196
Original
2017
Later among the works it cites.
A. Odena, C. Olah, and J. Shlens, “Conditional image synthesis with auxiliary classifier gans,” in Proceedings of the 34th International Conference on Machine Learning-Volume 70
2017
Later among the works it cites.
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “Gans trained by a two time-scale update rule converge to a local nash equilibrium,” in Advances in Neural Information Processing Systems
2017
Later among the works it cites.

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

Original

2017

Cited alongside, same era.

T. Xiao, J. Hong, and J. Ma, “Dna-gan: learning disentangled representations from multi-attribute images,” arXiv preprint arXiv:1711.05415

Original

2017

Cited alongside, same era.

M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein gan,” arXiv preprint arXiv:1701.07875

Original

2017

Cited alongside, same era.

X. Mao, Q. Li, H. Xie, R. Y. Lau, Z. Wang, and S. Paul Smolley, “Least squares generative adversarial networks,” in Proceedings of the IEEE International Conference on Computer Vision

2017

Cited alongside, same era.

T. Xiao, J. Hong, and J. Ma, “Elegant: Exchanging latent encodings with gan for transferring multiple face attributes,” in Proceedings of the European Conference on Computer Vision (ECCV)

2018

Later among the works it cites.

Y. Choi, M. Choi, M. Kim, J.-W. Ha, S. Kim, and J. Choo, “Stargan: Unified generative adversarial networks for multi-domain image-to-image translation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

2018

Later among the works it cites.

Z. He, W. Zuo, M. Kan, S. Shan, and X. Chen, “Attgan: Facial attribute editing by only changing what you want,” IEEE Transactions on Image Processing

2019

Closest in time.

M. Liu, Y. Ding, M. Xia, X. Liu, E. Ding, W. Zuo, and S. Wen, “Stgan: A unified selective transfer network for arbitrary image attribute editing,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

2019

Closest in time.