This was the classical intuition, but turned out to be untrue in the regime of large NNs.
The modern view is double descent (https://en.wikipedia.org/wiki/Double_descent), where small models generalize better until the number of parameters exceeds the number of training examples, then larger models generalize better with the same amount of data.
This was the classical intuition, but turned out to be untrue in the regime of large NNs.
The modern view is double descent (https://en.wikipedia.org/wiki/Double_descent), where small models generalize better until the number of parameters exceeds the number of training examples, then larger models generalize better with the same amount of data.