Progressive Growing of GANs for Improved Quality, Stability, and Variation.

Articles citing Progressive Growing of GANs for Improved Quality, Stability, and Variation.

Title / Author	Year
Stargan: Unified generative adversarial networks for multi-domain image-to-image translation Y Choi, M Choi, M Kim, JW Ha, S Kim, J Choo Proceedings of the IEEE conference on computer vision and pattern …	2018
High-resolution image synthesis and semantic manipulation with conditional gans TC Wang, MY Liu, JY Zhu, A Tao, J Kautz, B Catanzaro Proceedings of the IEEE conference on computer vision and pattern …	2018
Large scale gan training for high fidelity natural image synthesis A Brock, J Donahue, K Simonyan arXiv preprint arXiv:1809.11096	2018
A style-based generator architecture for generative adversarial networks T Karras, S Laine, T Aila Proceedings of the IEEE conference on computer vision and pattern …	2019
Self-attention generative adversarial networks H Zhang, I Goodfellow, D Metaxas, A Odena International Conference on Machine Learning, 7354-7363	2019
Residual dense network for image super-resolution Y Zhang, Y Tian, Y Kong, B Zhong, Y Fu Proceedings of the IEEE conference on computer vision and pattern …	2018
Multimodal unsupervised image-to-image translation X Huang, MY Liu, S Belongie, J Kautz Proceedings of the European Conference on Computer Vision (ECCV), 172-189	2018
Glow: Generative flow with invertible 1x1 convolutions DP Kingma, P Dhariwal Advances in neural information processing systems, 10215-10224	2018
Generative image inpainting with contextual attention J Yu, Z Lin, J Yang, X Shen, X Lu, TS Huang Proceedings of the IEEE conference on computer vision and pattern …	2018
Image inpainting for irregular holes using partial convolutions G Liu, FA Reda, KJ Shih, TC Wang, A Tao, B Catanzaro Proceedings of the European Conference on Computer Vision (ECCV), 85-100	2018
Mocogan: Decomposing motion and content for video generation S Tulyakov, MY Liu, X Yang, J Kautz Proceedings of the IEEE conference on computer vision and pattern …	2018
Which training methods for GANs do actually converge? L Mescheder, A Geiger, S Nowozin arXiv preprint arXiv:1801.04406	2018
Video-to-video synthesis TC Wang, MY Liu, JY Zhu, G Liu, A Tao, J Kautz, B Catanzaro arXiv preprint arXiv:1808.06601	2018
Stackgan++: Realistic image synthesis with stacked generative adversarial networks H Zhang, T Xu, H Li, S Zhang, X Wang, X Huang, DN Metaxas IEEE transactions on pattern analysis and machine intelligence 41 (8), 1947-1962	2018
A note on the inception score S Barratt, R Sharma arXiv preprint arXiv:1801.01973	2018
Neural scene representation and rendering SMA Eslami, DJ Rezende, F Besse, F Viola, AS Morcos, M Garnelo, ... Science 360 (6394), 1204-1210	2018
Free-form image inpainting with gated convolution J Yu, Z Lin, J Yang, X Shen, X Lu, TS Huang Proceedings of the IEEE International Conference on Computer Vision, 4471-4480	2019
A survey on image data augmentation for deep learning C Shorten, TM Khoshgoftaar Journal of Big Data 6 (1), 60	2019
Ganimation: Anatomically-aware facial animation from a single image A Pumarola, A Agudo, AM Martinez, A Sanfeliu, F Moreno-Noguer Proceedings of the European conference on computer vision (ECCV), 818-833	2018
Pros and cons of gan evaluation measures A Borji Computer Vision and Image Understanding 179, 41-65	2019

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