[FeepingCreature]

And my point is that is not what overfit is. Overfit is a specific problem where the network fails to recognize a commonality in the training set and instead interprets the irrelevant details of some subset of training samples (in the extreme, individual samples) as distinct properties.

Your example training set is not filled with noise that the network is picking up on to its detriment. Your example training set is simply not representative of the function you are trying to teach.

[karpierz]

I don't have an example training set. I don't have an example model.

My exact point is that if your test set isn't representative of the underlying distribution, then accuracy on the test set doesn't mean that your model isn't overfit.