Converting from RGB to YUV is absolutely subject to the bitter lesson because it is trying to generalize from a representation that we have seen works for some

Converting from RGB to YUV is absolutely subject to the bitter lesson because it is trying to generalize from a representation that we have seen works for some classical methods and hard code that knowledge in to the AI which could easily learn (and will anyways) a more useful representation for itself. > LLMs on character-level UTF-32 and just letting it figure everything out by itself, while needing two orders of magnitude bigger contexts and parameter counts. This was tried extensively and honestly it is probably still too early to proclaim the demise of this approach. It's also completely different - you're conflating a representation that literally changes the number of forward passes you have to do (ie. the amount of computation - what the bitter lesson is about) vs. one that (at most) would just require stacking on a few layers or so. A better example for your point (imo) would be audio recognition, where we pre-transform from wave amplitudes into log mel spectrogram for ingestion by the model. I think this will ultimately fall to the bitter lesson as well though. Also a key difference is that you are proposing going from methods that already work to try to inject more classical knowledge into them. It is oftentimes the case that you'll have an intermediary fusion between deep + classical, but not if you already have working fully deep methods.