Rezultati pretraživanja
  1. I've created an experimental GAN architecture I call or "Recursive-Residual GAN" and I am pretty astonished that: - it works at all - how well it works across a pretty wide range of scales. - it is just 15% the size of a comparable model

    Portrait generated by RecuResGAN - replication of training example
    Portrait generated by RecuResGAN - replication of training example
    Portrait generated by RecuResGAN - replication of training example
    Portrait generated by RecuResGAN - replication of training example
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  2. The principle is pretty simple: in a classic residual architecture you chain several residual blocks behind each other (in the default is 9 blocks), what I do in is to use a single block, but loop 9 times over it, feeding its output back into its input.

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