, https://github.com/bioinf-jku/TTUR 3. https://github.com/tkarras/progressive_growing_of_gans 4. Source code. http://thoth.inrialpes.fr/research/ganeval 5. Supplementary material, also available in arXiv technical report at https
Towards principled methods for training generative adversarial networks, p.ICLR, 2017. ,
Wasserstein generative adversarial networks, p.ICML, 2017. ,
A note on the Inception score. arXiv preprint, 2018. ,
BEGAN: Boundary equilibrium generative adversarial networks. arXiv preprint arXiv, pp.1703-10717, 2017. ,
Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p.CVPR, 2017. ,
DOI : 10.1109/CVPR.2017.18
Biomedical Data Augmentation Using Generative Adversarial Neural Networks, In: ICANN, vol.115, issue.3, 2017. ,
DOI : 10.1007/978-3-319-46630-9_13
Info- GAN: Interpretable representation learning by information maximizing generative adversarial nets, p.NIPS, 2016. ,
Good semisupervised learning that requires a bad GAN, p.NIPS, 2017. ,
ImageNet: A large-scale hierarchical image database, 2009 IEEE Conference on Computer Vision and Pattern Recognition, p.CVPR, 2009. ,
DOI : 10.1109/CVPR.2009.5206848
Deep generative image models using a laplacian pyramid of adversarial networks, p.NIPS, 2015. ,
Adversarially learned inference, p.ICLR, 2017. ,
A learned representation for artistic style, p.ICLR, 2017. ,
Synthetic data augmentation using GAN for improved liver lesion classification, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), p.ISBI, 2018. ,
DOI : 10.1109/ISBI.2018.8363576
URL : http://arxiv.org/pdf/1801.02385
Generative adversarial nets, p.NIPS, 2014. ,
Improved training of Wasserstein GANs, p.NIPS, 2017. ,
Identity Mappings in Deep Residual Networks, p.ECCV, 2016. ,
DOI : 10.1109/CVPR.2015.7298594
GANs trained by a two time-scale update rule converge to a local Nash equilibrium, p.NIPS, 2017. ,
Random decision forests, In: ICDAR, 1995. ,
Image-to-Image Translation with Conditional Adversarial Networks, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p.CVPR, 2017. ,
DOI : 10.1109/CVPR.2017.632
URL : http://arxiv.org/pdf/1611.07004
Progressive growing of GANs for improved quality, stability, and variation, p.ICLR, 2018. ,
Adam: A method for stochastic optimization, p.ICLR, 2015. ,
Auto-encoding variational Bayes, p.ICLR, 2014. ,
Learning multiple layers of features from tiny images, Tech. rep, 2009. ,
ImageNet classification with deep convolutional neural networks, Communications of the ACM, vol.60, issue.6, p.NIPS, 2012. ,
DOI : 10.1162/neco.2009.10-08-881
URL : http://dl.acm.org/ft_gateway.cfm?id=3065386&type=pdf
Gradient-based learning applied to document recognition, Proceedings of the IEEE, vol.86, issue.11, pp.2278-2324, 1998. ,
DOI : 10.1109/5.726791
Photo-realistic single image superresolution using a generative adversarial network, p.CVPR, 2017. ,
DOI : 10.1109/cvpr.2017.19
URL : http://arxiv.org/pdf/1609.04802
Are GANs created equal? A large-scale study. arXiv preprint arXiv, pp.1711-10337, 2017. ,
Visualizing data using t-SNE, JMLR, vol.9, pp.2579-2605, 2008. ,
Least Squares Generative Adversarial Networks, 2017 IEEE International Conference on Computer Vision (ICCV), p.ICCV, 2017. ,
DOI : 10.1109/ICCV.2017.304
URL : http://arxiv.org/pdf/1611.04076
Conditional generative adversarial nets. arXiv preprint arXiv:1411, p.1784, 2014. ,
Spectral normalization for generative adversarial networks, p.ICLR, 2018. ,
cGANs with projection discriminator, In: ICLR, 2018. ,
Synthesizing realistic neural population activity patterns using generative adversarial networks, p.ICLR, 2018. ,
Reconstruction of three-dimensional porous media using generative adversarial neural networks, Physical Review E, vol.1294, issue.4, p.43309, 2017. ,
DOI : 10.1093/mnrasl/slx008
f-GAN: Training generative neural samplers using variational divergence minimization, p.NIPS, 2016. ,
Conditional image synthesis with auxiliary classifier GANs, p.ICML, 2017. ,
Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multilayer Calorimeters, Physical Review Letters, vol.8, issue.4, p.42003, 2018. ,
DOI : 10.1088/1674-1137/38/9/090001
Scikit-learn: Machine learning in Python, pp.2825-2830, 2011. ,
URL : https://hal.archives-ouvertes.fr/hal-00650905
Wasserstein Barycenter and Its Application to Texture Mixing, Intl. Conf. Scale Space and Variational Methods in Computer Vision, 2011. ,
DOI : 10.1109/18.119725
Unsupervised representation learning with deep convolutional generative adversarial networks, p.ICLR, 2016. ,
Improved techniques for training GANs, p.NIPS, 2016. ,
PixelCNN++: Improving the PixelCNN with discretized logistic mixture likelihood and other modifications, p.ICLR, 2017. ,
Continual learning with deep generative replay, p.NIPS, 2017. ,
Learning from Simulated and Unsupervised Images through Adversarial Training, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p.CVPR, 2017. ,
DOI : 10.1109/CVPR.2017.241
URL : http://arxiv.org/pdf/1612.07828
Going deeper with convolutions, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p.CVPR, 2015. ,
DOI : 10.1109/CVPR.2015.7298594
A note on the evaluation of generative models, p.ICLR, 2016. ,
A Bayesian data augmentation approach for learning deep models, p.NIPS, 2017. ,
Pixel recurrent neural networks, In: ICML, 2016. ,
Low-shot learning from imaginary data, p.CVPR, 2018. ,
DOI : 10.1109/cvpr.2018.00760
URL : http://arxiv.org/pdf/1801.05401
Predicting Rapid Fire Growth (Flashover) Using Conditional Generative Adversarial Networks, Electronic Imaging, vol.2018, issue.9, 2018. ,
DOI : 10.2352/ISSN.2470-1173.2018.09.SRV-127
URL : http://arxiv.org/pdf/1801.09804
Stack- GAN: Text to photo-realistic image synthesis with stacked generative adversarial networks, p.ICCV, 2017. ,
Energy-based generative adversarial networks, p.ICLR, 2017. ,
Camera style adaptation for person re-identification, p.CVPR, 2018. ,
Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks, 2017 IEEE International Conference on Computer Vision (ICCV), p.ICCV, 2017. ,
DOI : 10.1109/ICCV.2017.244