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SinGAN: Learning a Generative Model from a Single Natural Image: Difference between revisions
SinGAN: Learning a Generative Model from a Single Natural Image (view source)
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<math>\{z_N^{rec}, z_{N-1}^{rec}, ..., z_0^{rec}\} = \{z^*, 0, ..., 0\}</math> | <math>\{z_N^{rec}, z_{N-1}^{rec}, ..., z_0^{rec}\} = \{z^*, 0, ..., 0\}</math> | ||
where the initial noise <math>z^*</math> is drawn once and then fixed during the rest of the training. | where the initial noise <math>z^*</math> is drawn once and then fixed during the rest of the training. | ||
==Applications== | |||
The following are applications they identify. | |||
The basic idea for each of these applications is to start at an intermediate layer rather than the bottom layer.<br> | |||
While the bottom layer is a purely unconditional GAN, the intermediate generators are more akin to conditional GANs. | |||
===Super-Resolution=== | |||
===Paint-to-Image=== | |||
===Harmonization=== | |||
===Editing=== | |||
===Single Image Animation=== | |||
==Repo== | |||
The official repo for SinGAN can be found on their [https://github.com/tamarott/SinGAN Github Repo]<br> | |||