[geysersam]

Yeah I agree that was badly phrased. I meant data points not used in any aspect of the training procedure. Such as a photo taken after the image recognition model have been trained.

It's widely recognized that image recognition models typically perform well also on such data. We don't need to quantify that exactly to conclude that many large (in terms of parameters) models generalize quite well to data neither in the training or the test set.

Provided that the model space is large enough to contain both models that generalize well and models that don't (while still fitting the training data), some explanation why we tend to find generalizing models is required.

[YeGoblynQueenne]

Thank you for clarifying!

>> It's widely recognized that image recognition models typically perform well also on such data. We don't need to quantify that exactly to conclude that many large (in terms of parameters) models generalize quite well to data neither in the training or the test set.

I disagree. That is exactly what we need to quantify with great care, precisely because if it were true, an explanation would be needed.

As I say above, and as far as I'm aware, nobody bothers to do this quantification and so any "widely recognised" idea that models generalise to unseen data is only hearsay.