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I've created an experimental GAN architecture I call #RecuResGAN 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 #pix2pixHD model
- it works at all
- how well it works across a pretty wide range of scales.
- it is just 15% the size of a comparable #pix2pixHD model
Of course I did not google the concept of recursive neural networks before I started this experiment and enjoyed the illusion of being very innovative here for a whole day:
en.wikipedia.org/wiki/Recursive…
en.wikipedia.org/wiki/Recursive…
The principle is pretty simple: in a classic residual architecture you chain several residual blocks behind each other (in #pix2pixHD the default is 9 blocks), what I do in #RecuResGAN is to use a single block, but loop 9 times over it, feeding its output back into its input.
