Why validation accuracy starts to increase after overfitting? I'm training a model on a small dataset of images. following are the curves of accuracy, f1 score and auc score. it's clear that the model is overfitting, however I don't understand why after sometime its validation results starts to improve. Can anyone explain to me please. 


it's clear that the model is overfitting

No it's not. It shows that you just needed to train the model longer for it to start to converge. In most cases, we don't know how long should the training take. It would be clear if you reached a loss equal to zero, or 100% accuracy, but otherwise, we don't know. That is why it is often worth trying different optimization algorithms and hyperparameters (learning rate, batch size), and the “train a little bit longer” approach.
