Overfitting is, by definition, learning a model that doesn't generalize to the distribution of inputs you care about. If your validation set has the same distribution as the inputs you care about, then your definition holds. But that's definitely not true in practice. Usually the data you collect won't be exactly representative of the conditions you're looking to test, unless your problem is very simple.
> Overfitting is, by definition, learning a model that doesn't generalize to the distribution of inputs you care about.
No, that's just mis-modelling. Overfitting is specifically doing so in a way that learns the training data too well, at the cost of generalizing. If you try and have a single layer perception network classify a nonlinear function, it will fail to generalize. But it certainly isn't "overfitting".
Overfitting is not the only form of mistake when training a model. You've presented a different one, which is just like trying to train on misrepresentative data. But that isn't "overfitting", it's just having bad data. Your model isn't "failing to generalize", it has nothing to generalize over.
The classic demonstration of this is that overfitting usually results in a accuracy curve that "frowns" on validation data. Your accuracy peaks, but then decreases as you learn the structure of the test data instead of the general structure. In your example that won't happen.
Training a model in the wrong problem isn't overfitting. In fact, your example is more like underfitting than overfitting. The model in your example would fail to see the full complexity of the structure, instead of as in overfitting, make it more complicated than reality.
