Bidirectional Conditional Generative Adversarial Networks

LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based Learning Applied to Document Recognition. Proceedings of the IEEE 86

1998

Earlier work this paper cites.

Hinton, G.E., Salakhutdinov, R.R.: Reducing the Dimensionality of Data with Neural Networks. science 313

2006

Earlier work this paper cites.

Aubry, M., Maturana, D., Efros, A., C. Russell, B., Sivic, J.: Seeing 3d chairs: Exemplar part-based 2d-3d alignment using a large dataset of cad models (06 2014)

2014

Earlier work this paper cites.

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Nets. In: Advances in neural information processing systems. pp. 2672–2680 (2014)

2014

Earlier work this paper cites.

Kingma, D.P., Welling, M.: Auto-encoding Variational Bayes. In: International Conference on Learning Representations (2014)

2014

Earlier work this paper cites.

Mirza, M., Osindero, S.: Conditional Generative Adversarial Nets. arXiv preprint arXiv:1411.1784 (2014)

Original

2014

Earlier work this paper cites.

Liu, Z., Luo, P., Wang, X., Tang, X.: Deep Learning Face Attributes in the Wild. In: Proceedings of International Conference on Computer Vision (ICCV) (Dec 2015)

2015

Earlier work this paper cites.

Parkhi, O.M., Vedaldi, A., Zisserman, A.: Deep face recognition. In: British Machine Vision Conference (2015)

2015

Earlier work this paper cites.

Belharbi, S., Hérault, R., Chatelain, C., Adam, S.: Deep multi-task learning with evolving weights. In: European Symposium on Artificial Neural Networks (ESANN) (2016)

2016

Earlier work this paper cites.

Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I.: Adversarial Autoencoders. In: International Conference on Learning Representations (2016), http://arxiv.org/abs/1511.05644

Original

2016

Earlier work this paper cites.

Mathieu, M.F., Zhao, J.J., Zhao, J., Ramesh, A., Sprechmann, P., LeCun, Y.: Disentangling Factors of Variation in Deep Representation using Adversarial Training. In: Advances in Neural Information Processing Systems. pp. 5040–5048 (2016)

2016

Earlier work this paper cites.

Perarnau, G., Weijer, J.v.d., Raducanu, B., Álvarez, J.M.: Invertible Conditional GANs for image editing. In: NIPS Workshop on Adversarial Training (2016)

2016

Cited alongside, same era.

Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., Lee, H.: Generative Adversarial Text-to-Image Synthesis. In: Proceedings of The 33rd International Conference on Machine Learning (2016)

2016

Cited alongside, same era.

Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for Training gans. In: Advances in Neural Information Processing Systems. pp. 2234–2242 (2016)

2016

Cited alongside, same era.

Sixt, L., Wild, B., Landgraf, T.: Rendergan: Generating realistic labeled data. arXiv preprint arXiv:1611.01331 (2016)

Original

2016

Cited alongside, same era.

Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-To-Image Translation With Conditional Adversarial Networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Jul 2017)

2017

Closest in time.

Kaneko, T., Hiramatsu, K., Kashino, K.: Generative Attribute Controller With Conditional Filtered Generative Adversarial Networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Jul 2017)

2017

Closest in time.

Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., Shi, W.: Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Jul 2017)

2017

Closest in time.

Li, J., Liang, X., Wei, Y., Xu, T., Feng, J., Yan, S.: Perceptual Generative Adversarial Networks for Small Object Detection. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Jul 2017)