Deep Compressed Sensing
Wu et al.: https://arxiv.org/pdf/1905.06723.pdf
#deeplearning #generativeadversarialnetworks #technology

SinGAN: Learning a Generative Model from a Single Natural Image
Shaham et al.: https://arxiv.org/abs/1905.01164v1
#ArtificialIntelligence #DeepLearning #GenerativeAdversarialNetworks

Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
Zakharov et al.: https://arxiv.org/abs/1905.08233
#ComputerVision #GenerativeAdversarialNetworks #MachineLearning

Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
Zakharov et al.: https://arxiv.org/abs/1905.08233
Video: https://youtu.be/p1b5aiTrGzY
Animating heads using only few shots of target person (or even 1 shot). Keypoints, adaptive instance norms and GANs.
#ArtificialIntelligence #DeepLearning #GenerativeAdversarialNetworks

Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
Zakharov et al.: https://arxiv.org/abs/1905.08233

@ArtificialIntelligenceArticles

#ComputerVision #GenerativeAdversarialNetworks #MachineLearning

"Wasserstein GAN"

Written by James Allingham: http://www.depthfirstlearning.com/2019/WassersteinGAN

#DeepLearning #GenerativeModels #GenerativeAdversarialNetworks

SinGAN: Learning a Generative Model from a Single Natural Image
Shaham et al.: https://arxiv.org/abs/1905.01164v1
#ArtificialIntelligence #DeepLearning #GenerativeAdversarialNetworks

Make music with GANs

GANSynth is a new method for fast generation of high-fidelity audio.

🎵 Examples: http://goo.gl/magenta/gansynth-examples
⏯ Colab: http://goo.gl/magenta/gansynth-demo
📝 Paper: http://goo.gl/magenta/gansynth-paper
💻 Code: http://goo.gl/magenta/gansynth-code
⌨️ Blog: http://magenta.tensorflow.org/gansynth



#artificialintelligence #deeplearning #generativeadversarialnetworks

GAN Lab: Play with Generative Adversarial Networks (GANs) in your browser!
By created by Minsuk Kahng, Nikhil Thorat, Polo Chau, Fernanda Viégas, and Martin Wattenberg: https://poloclub.github.io/ganlab/
Research paper: https://minsuk.com/research/papers/kahng-ganlab-vast2018.pdf
#AI #ArtificialIntelligence #GenerativeAdversarialNetworks

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

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

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

Hair-GANs: Recovering 3D Hair Structure from a Single Image
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

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

Seeing What a GAN Cannot Generate

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

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

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

Improving sample diversity of a pre-trained, class-conditional GAN by changing its class embeddings
Qi Li, Long Mai, Anh Nguyen: http://anhnguyen.me/project/biggan-am/
#DeepLearning #GenerativeAdversarialNetworks #GAN