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NIPS 2016 Tutorial: Generative Adversarial Networks

2016-12-31 · Ian Goodfellow

This report summarizes the tutorial presented by the author at NIPS 2016 on generative adversarial networks (GANs). The tutorial describes: (1) Why generative modeling is a topic worth studying, (2) how generative models work, and how GANs compare to other generative models, (3) the details of how GANs work, (4) research frontiers in GANs, and (5) state-of-the-art image models that combine GANs with other methods. Finally, the tutorial contains three exercises for readers to complete, and the solutions to these exercises.

📄 PDF Abstract BibTeX arXiv:1701.00160

Code (21)

Ereebay/CV-Documents tf
Ereebay/Deep-Learning-Documents tf
Ereebay/DeepLearningDocuments tf
FelSiq/machine-learning-learning pytorch
HaijunMa/GAN pytorch
HaijunMa/GAN-Getting-started-learning pytorch
Natsu6767/DCGAN-PyTorch pytorch
Puneethnaik/Generative-Adversarial-Networks
Wlodder/Fashion-Gen tf
adityabingi/DCGAN-TF2.0 tf
bardank/dc-gan pytorch
davidhalladay/CE-compressive-sensing-on-generative-model-pytorch pytorch
jparcill/gansbestfriend pytorch
prudhvirajboddu/TensorFlowML tf
rajprakrit/DCGAN pytorch
richardrl/gan-pytorch pytorch
rohitkuk/AnimeGAN pytorch
rohitkuk/Cartoonify pytorch
rohitpatwa/gans-mode-collapse pytorch
runwayml/Intro-Synthetic-Media tf
shreyas-kowshik/Generative-Models-Tensorflow tf

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