I do think it’s a form of overfitting - loss on the training set improved while loss on the validation set got worse. However, it’s not the common form of overf

I do think it’s a form of overfitting - loss on the training set improved while loss on the validation set got worse. However, it’s not the common form of overfitting, where accuracy on the validation set gets worse. In this case, accuracy on the validation data set continued to improve. But when it was wrong, it gave a higher confidence in its wrong answer than before. e.g. before it may have incorrectly thought the answer was X, with 60% confidence, now it still thinks the answer is X, but with higher confidence, say 70%. I do think it’s a form of overfitting, but a weird one. Overconfidence seems like a good, more specific term to me.