Reference-GAN (updated on 2/28/2020)

https://pathmind.com/wiki/generative-adversarial-network-gan

Notes:

  • Discriminative models learn the boundary between classes
  • Generative models model the distribution of individual classes
i.e. the discriminator decides whether each instance of data that it reviews belongs to the actual training dataset or not.

Here are the steps a GAN takes:
  • The generator takes in random numbers and returns an image.
  • This generated image is fed into the discriminator alongside a stream of images taken from the actual, ground-truth dataset.
  • The discriminator takes in both real and fake images and returns probabilities, a number between 0 and 1, with 1 representing a prediction of authenticity and 0 representing fake.

Notes:

However, current VR/MR technologies present a fundamental challenge: to present images at the extremely high resolution required for immersion places enormous demands on the rendering engine and transmission process. Headsets often have insufficient display resolution, which can limit the field of view, worsening the experience

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