lets say AI is not-so-smart JPEG which has more parts missing, and there is more guesswork when producing restoration. compression is most of the times about f

lets say AI is not-so-smart JPEG which has more parts missing, and there is more guesswork when producing restoration. compression is most of the times about finding the minimal grammar that unfolds to the same original material. interestingly Fabrice Bellard somehow found a way to use transformers for compression without loss, and beats xz by significant margin. https://bellard.org/nncp/nncp_v2.1.pdf https://bellard.org/nncp/nncp_v2.1.pdf. it uses "deterministic mode of PyTorch" to make sure both directions work alike which I guess means - it saves the random toss throughout compression, for the decompression to use. note: this paper is still on my to-read list.