[vandreas2]

OK I think I got what you're saying, is that memorizing means that it will perform only on data it has seen in training that is exactly identical to the current one, and it will fail on even slight variations. My question then becomes what's the boundary of that extrapolation in order for it to be considered learning vs memorization? For example to come back to my earlier example, let's say a model is trained on photos of cars taken from only some angles, then it should be able to extrapolate to the intermediate ones without having seen the exact same photo. A large dataset would ensure that it has enough angles to be able to do that right? But there is no amount of photos that will make it identify a car from an underneath photo if it hasn't already seen one from underneath I wouldn't think. So this is the limit of 'learning' as opposed to 'memorization'?

[atty]

Yes, your explanation is essentially correct. There is work done in the areas you’re talking about - essentially forcing models to more explicitly learn “concepts” - and in very large language models that seems to be emerging naturally. But current vision models would almost certainly break when trying to identify a vehicle from the bottom shot if it had never seen a vehicles undercarriage during training. Current vision models are capable of identifying vehicles from arbitrary angles (when viewed from the side/head on) and in arbitrary shades/colors/models/etc, and that’s about the amount of extrapolation we’d be talking about.

[pietro72ohboy]

I don’t think your premise is correct. The holy grail of such systems - Human Intelligence- will also break similarly if it’s asked to identify a car from an undercarriage when the human subject has never ever seen an undercarriage. We really forget how much data humans are able to expose themselves to in their formative years. I’d often bend down to fetch my ball that had accidentally slid under a parked car and that’s how I learnt about the look of undercarriages.