Gregory Stein’s Favorite Deep Learning Papers of 2017

Greg provides a pick of the crop of deep-learning papers from 2017 along with explanations for what makes these papers special.

The list includes:

  • Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
  • Wasserstein GAN
  • Learning from Simulated and Unsupervised Images through Adversarial Training
  • Mastering the game of Go without human knowledge
  • Deep Image Prior

 

Via cachestocaches.com

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