Generative Adversarial Networks (GANs) in 50 lines of code (PyTorch)
tl;dr: GANs are simpler to set up than you think
Blog by Dev Nag : https://medium.com/@devnag/generative-adversarial-networks-gans-in-50-lines-of-code-pytorch-e81b79659e3f
#deeplearning #generativeadversarialnetworks #pytorch
tl;dr: GANs are simpler to set up than you think
Blog by Dev Nag : https://medium.com/@devnag/generative-adversarial-networks-gans-in-50-lines-of-code-pytorch-e81b79659e3f
#deeplearning #generativeadversarialnetworks #pytorch
Medium
Generative Adversarial Networks (GANs) in 50 lines of code (PyTorch)
tl;dr: GANs are simpler to set up than you think
Generative Adversarial Networks (GANs) in 50 lines of code (PyTorch)
tl;dr: GANs are simpler to set up than you think
Blog by Dev Nag : https://medium.com/@devnag/generative-adversarial-networks-gans-in-50-lines-of-code-pytorch-e81b79659e3f
#deeplearning #generativeadversarialnetworks #pytorch
tl;dr: GANs are simpler to set up than you think
Blog by Dev Nag : https://medium.com/@devnag/generative-adversarial-networks-gans-in-50-lines-of-code-pytorch-e81b79659e3f
#deeplearning #generativeadversarialnetworks #pytorch
Medium
Generative Adversarial Networks (GANs) in 50 lines of code (PyTorch)
tl;dr: GANs are simpler to set up than you think
Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model
David John Gagne II, Hannah M. Christensen, Aneesh C. Subramanian, Adam H. Monahan : https://arxiv.org/abs/1909.04711
#GenerativeAdversarialNetworks #MachineLearning #Physics
David John Gagne II, Hannah M. Christensen, Aneesh C. Subramanian, Adam H. Monahan : https://arxiv.org/abs/1909.04711
#GenerativeAdversarialNetworks #MachineLearning #Physics
arXiv.org
Machine Learning for Stochastic Parameterization: Generative...
Stochastic parameterizations account for uncertainty in the representation of
unresolved sub-grid processes by sampling from the distribution of possible
sub-grid forcings. Some existing...
unresolved sub-grid processes by sampling from the distribution of possible
sub-grid forcings. Some existing...
Hair-GANs: Recovering 3D Hair Structure from a Single Image
Meng Zhang Youyi Zheng : https://arxiv.org/pdf/1811.06229.pdf
#Hair #DeepLearning #GenerativeAdversarialNetworks
Meng Zhang Youyi Zheng : https://arxiv.org/pdf/1811.06229.pdf
#Hair #DeepLearning #GenerativeAdversarialNetworks
Connections between Support Vector Machines, Wasserstein distance and gradient-penalty GANs
Alexia Jolicoeur-Martineau and Ioannis Mitliagkas : https://arxiv.org/abs/1910.06922
#GenerativeAdversarialNetworks #RelativisticGAN #SVM
Alexia Jolicoeur-Martineau and Ioannis Mitliagkas : https://arxiv.org/abs/1910.06922
#GenerativeAdversarialNetworks #RelativisticGAN #SVM
Deep Learning course: lecture slides and lab notebooks
Built and maintained by Olivier Grisel and Charles Ollion: https://m2dsupsdlclass.github.io/lectures-labs/
#ArtificialIntelligence #DeepLearning #GenerativeAdversarialNetworks #MachineLearning #NeuralNetworks
Built and maintained by Olivier Grisel and Charles Ollion: https://m2dsupsdlclass.github.io/lectures-labs/
#ArtificialIntelligence #DeepLearning #GenerativeAdversarialNetworks #MachineLearning #NeuralNetworks
lectures-labs
Deep Learning course: lecture slides and lab notebooks
Slides and Jupyter notebooks for the Deep Learning lectures at Master Year 2 Data Science from Institut Polytechnique de Paris
Seeing What a GAN Cannot Generate
Bau et al.: https://arxiv.org/abs/1910.11626
#ArtificialIntelligence #DeepLearning #GenerativeAdversarialNetworks
Bau et al.: https://arxiv.org/abs/1910.11626
#ArtificialIntelligence #DeepLearning #GenerativeAdversarialNetworks
Connections between Support Vector Machines, Wasserstein distance and gradient-penalty GANs
Alexia Jolicoeur-Martineau, Ioannis Mitliagkas : https://arxiv.org/abs/1910.06922
#PyTorch code: https://github.com/AlexiaJM/MaximumMarginGANs
#SupportVectorMachines #GenerativeAdversarialNetworks
Alexia Jolicoeur-Martineau, Ioannis Mitliagkas : https://arxiv.org/abs/1910.06922
#PyTorch code: https://github.com/AlexiaJM/MaximumMarginGANs
#SupportVectorMachines #GenerativeAdversarialNetworks
arXiv.org
Gradient penalty from a maximum margin perspective
A popular heuristic for improved performance in Generative adversarial networks (GANs) is to use some form of gradient penalty on the discriminator. This gradient penalty was originally motivated...
Connections between Support Vector Machines, Wasserstein distance and gradient-penalty GANs
Alexia Jolicoeur-Martineau, Ioannis Mitliagkas : https://arxiv.org/abs/1910.06922
#GenerativeAdversarialNetworks #MachineLearning #SupportVectorMachines
Alexia Jolicoeur-Martineau, Ioannis Mitliagkas : https://arxiv.org/abs/1910.06922
#GenerativeAdversarialNetworks #MachineLearning #SupportVectorMachines
arXiv.org
Gradient penalty from a maximum margin perspective
A popular heuristic for improved performance in Generative adversarial networks (GANs) is to use some form of gradient penalty on the discriminator. This gradient penalty was originally motivated...
Analyzing and Improving the Image Quality of StyleGAN
Karras et al.:https://arxiv.org/abs/1912.04958
Github: https://github.com/NVlabs/stylegan2
#ArtificialIntelligence #DeepLearning #GenerativeAdversarialNetworks
Karras et al.:https://arxiv.org/abs/1912.04958
Github: https://github.com/NVlabs/stylegan2
#ArtificialIntelligence #DeepLearning #GenerativeAdversarialNetworks
GitHub
GitHub - NVlabs/stylegan2: StyleGAN2 - Official TensorFlow Implementation
StyleGAN2 - Official TensorFlow Implementation. Contribute to NVlabs/stylegan2 development by creating an account on GitHub.
Improving sample diversity of a pre-trained, class-conditional GAN by changing its class embeddings
Qi Li, Long Mai, Anh Nguyen: https://anhnguyen.me/project/biggan-am/
#DeepLearning #GenerativeAdversarialNetworks #GAN
Qi Li, Long Mai, Anh Nguyen: https://anhnguyen.me/project/biggan-am/
#DeepLearning #GenerativeAdversarialNetworks #GAN