[taeric]

Something amusing about needing a ridiculously large model to claim good generalization. Analytical models typically go the other way, right?

[bitL]

It's Deep Learning, not much to do with any analytical model, it's not thinking like a human :-(. Recently even good NLP processing needs 24GB+ for training (won't fit into 16GB), a good quality colorizing (no spills, natural colors) could be expected to be as demanding.

From the article:

"BEEFY Graphics card. I'd really like to have more memory than the 11 GB in my GeForce 1080TI (11GB). You'll have a tough time with less. The Unet and Critic are ridiculously large but honestly I just kept getting better results the bigger I made them."

[taeric]

I get that. I just have a hard time thinking that is "generalizing" the model, so much as making the model all encompassing.

[tripzilch]

It's the difference between the sense of training the model to be a "generalist" and it doing "generalizing".

I strongly doubt that you can "generalize" colourization in the sense that you talk about (over a wide variety of subject matter).