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I haven't studied diffusion methods in great detail, but my simplistic ELI5 understanding goes something like: during training you gradually add noise to an ima
by mrtranscendence 3y ago
I haven't studied diffusion methods in great detail, but my simplistic ELI5 understanding goes something like: during training you gradually add noise to an image, step by step, and then the model learns how to remove the noise at each step. Adding noise to an image is, of course, very easy to do.
But for this approach adding "noise" would be much less straightforward, wouldn't it? You'd have to have some way to work out how to take "Berlin is the capital city of Germany with ..." down to an empty string in a way that marginally removes detail. For some passages this could be rather difficult to do. I feel like gathering training data would be a huge trial here.
- 317070 3y agoYes, the noising-denoising steps in this approach are summarizing-extending. The idea is that (unlike images) any piece of text lies somewhere on the length scale, like a fractal. You could take the whole text and treat it as a large scale object, or take shorter sentences and treat them on the short scale. What you are right in, is that you need a dataset of things being described twice. Like abstracts and papers, or messages and tldr's.