not in the paper, but I found a table on their site: https://xetdata.com/why-xethub/

Thank you for your question, it generated different points of view, from which I learned a lot.

Hi, you should always have an independent test split, and do whatever you want with the other, e.g. Crossvalidation visual sklearn reference

Why are you losing lots of datapoint in the test split? the idea is that distributions match so you can use the p-value criteria for this.

If you want to test lots of models try, optuna for finding the best hparams. No problem using the same metric, that's the one you care at the end.

Depending on your domain I would ignore step 5, because you can test disfribution shifts, and even new models in time and be able to compare them.

Great work, why don't you try stable diffusion? I think the topic has enough momemtum to boost your channel.

try https://rapids.ai/