- Makes skip connections between the generator and discriminator more
extensible by adding additional configuration options for them and supporting
1 and 0 skips.
- Places the temp/ directory with sample images from the training process appear
in the training directory instead of the codes/ directory.
This network is just a fixed (pre-trained) generator
that performs a corruption transformation that the
generator-in-training is expected to undo alongside
SR.
It now injects noise directly into the input filters, rather than a
pure noise filter. The pure noise filter was producing really
poor results (and I'm honestly not quite sure why).
Implements a ResGenv2 architecture which slightly increases the complexity
of the final output layer but causes it to be shared across all skip outputs.
This is a simpler resnet-based generator which performs mutations
on an input interspersed with interpolate-upsampling. It is a two
part generator:
1) A component that "fixes" LQ images with a long string of resnet
blocks. This component is intended to remove compression artifacts
and other noise from a LQ image.
2) A component that can double the image size. The idea is that this
component be trained so that it can work at most reasonable
resolutions, such that it can be repeatedly applied to itself to
perform multiple upsamples.
The motivation here is to simplify what is being done inside of RRDB.
I don't believe the complexity inside of that network is justified.
