If we are considering the function to be the neural network with an argmax applied to the output probabilities, it's not overfitting at all. Its classification

If we are considering the function to be the neural network with an argmax applied to the output probabilities, it's not overfitting at all. Its classification accuracy over unseen data (validation set) continues to improve. The issue here is one of calibration: https://en.m.wikipedia.org/wiki/Calibration_(statistics) https://en.m.wikipedia.org/wiki/Calibration_(statistics). That is, the output probabilities of the neural network do not reflect the true (observed) probabilities. If it is systematically underestimating the probabilities, it is termed "underconfident", and if overestimating the probabilities, "overconfident". Note that in these cases, it may still be improving as a classifier on unseen data, while still showing higher validation loss as calibration degrades.