3 ms·
Don't think that's applicable in this case. This "FeatUp" technique does not feed its output back into the model in any way. Rather, it's just producing a hig
by ta8645 3y ago
Don't think that's applicable in this case. This "FeatUp" technique does not feed its output back into the model in any way.
Rather, it's just producing a higher resolution output by taking multiple passes of the input image (subtly shifting the input image before each pass) producing a slightly different low-resolution feature map.
Each of these low-resolution feature maps represent contributions from differing areas of the input image. "FeatUp" can then create a higher-resolution feature map, "simply" by taking the color from the pass with the most appropriate input shift.
A very rough sketch:
Input Image: abcdefgh
Create multiple low resolution feature maps using your model,
shifting the input image, a few pixels each pass:
Pass 1: abcdefgh --> ACEG
Pass 2: bcdefgh --> BDFH
Now take all the low resolution feature passes
and combine into a single higher resolution version:
FeatUp: ACEG,BDFH --> ABCDEFGH
- pksebben 3y agoI wonder what you'd get if you did something similar on the latent space in a diffusion model, before decoding to an image.