Awesome-Dataset-Distillation VS Cold-Diffusion-Models

Continue Reading | Check out the paper and github link.

https://arxiv.org/abs/2208.11311 https://github.com/Guang000/Awesome-Dataset-Distillation

Sure you can. You might be interested in cold diffusion (https://arxiv.org/abs/2208.09392) which tries doing a bunch of different kinds of degradation processes besides adding gaussian noise. You can kind of choose whatever input corruption process you want and teach the model to reverse it, and it works kinda well (I think gaussian noise might be better though)

I wrote a response yesterday but did not post it or send it, ops.

I still don't understand the problem, if you ask model trained on a noise pattern "trees" for a forest it will still give you a random forest, that's what it was trained on, also: https://arxiv.org/abs/2208.09392, to see the diffusion process applied to processes other than Gaussian noise.

What about this research: https://arxiv.org/abs/2208.09392

Found relevant code at https://github.com/arpitbansal297/Cold-Diffusion-Models + all code implementations here

This claims to explain diffusion models from first principles, but the issue with explaining how they work is we don't know how they work.

The explanation in the original paper turns out not to be true; you can get rid of most of their assumptions and it still works: https://arxiv.org/abs/2208.09392

Check out the cold-diffusion repo, which has nice clean implementations, and also is useful in pointing out that the multi-step computation idea isn’t limited to denoising. https://github.com/arpitbansal297/Cold-Diffusion-Models