Generating Photorealistic Images of Fake Celebrities with Artificial Intelligence – NVIDIA Developer News Center

Researchers from @NVIDIA used #GANs to generate photorealistic images of fake celebrities.

  • Researchers from NVIDIA recently published a paper detailing their new methodology for generative adversarial networks (GANs) that generated photorealistic pictures of fake celebrities.
  • Rather than train a single neural network to recognize pictures, researchers train two competing networks.
  • “The key idea is to grow both the generator and discriminator progressively:  starting from a low resolution, we add new layers that model increasingly fine details as training progresses,” explained the researchers in their paper Progressive Growing of GANs for Improved Quality, Stability and Variation.
  • Since the publicly available CelebFaces Attributes (CelebA) training dataset varied in resolution and visual quality — and not sufficient enough for high output resolution — the researchers generated a higher-quality version of the dataset consisting of 30,000 images at 1024 x 1024 resolution.
  • Generating convincing realistic images with GANs are within reach and the researchers plan to use TensorFlow and multi-GPUs for the next part of the work.

Researchers from NVIDIA recently published a paper detailing their new methodology for generative adversarial networks (GANs) that generated photorealistic pictures of fake celebrities.
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The major advancements in Deep Learning in 2016

The major advancements in Deep Learning in 2016

  • In a nutshell, InfoGAN is able to generate representations that contain information about the dataset in an unsupervised way.
  • This model achieves state of the art results in all but POS tagging (where it came out in second place).
  • It goes a step further, training a single model that is able to translate between multiple pairs of languages.
  • Called Generative Adversarial Networks , it has enabled models to tackle unsupervised learning.
  • The model is trained separately for each pair of languages like Chinese-English.

Deep Learning has been the core topic in the Machine Learning community the last couple of years and 2016 was not the exception. In this article, we will go through the advancements we think have contributed the most (or have the potential) to move the field forward and how organizations and the community are making sure that these powerful technologies are going to be used in a way that is beneficial for all.
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