> Compression implies the input can be reconstructed from the output (lossy or not), in the case of these ml models the input is the training data and the outpu

> Compression implies the input can be reconstructed from the output (lossy or not), in the case of these ml models the input is the training data and the output is the model. You can't reconstruct even a fraction of that training data using the model alone therefore it is not compression even in the most lossy sense. It's already proven that you can reconstruct at least a small fraction of the training set from diffusion models. It's something quite well known, so could we not die on this hill? [1] https://twitter.com/Eric_Wallace_/status/1620449942090420224 https://twitter.com/Eric_Wallace_/status/1620449942090420224 [2] The paper: https://arxiv.org/abs/2301.13188 https://arxiv.org/abs/2301.13188 [3] Relevant HN thread: https://news.ycombinator.com/item?id=34596187 https://news.ycombinator.com/item?id=34596187