4 ms·
Is a learned downsampler a form of inverse crime? https://arxiv.org/abs/math-ph/0401050 https://arxiv.org/abs/math-ph/0401050
by frozenport 3y ago
Is a learned downsampler a form of inverse crime? https://arxiv.org/abs/math-ph/0401050 https://arxiv.org/abs/math-ph/0401050
- ta8645 3y agoDon'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.