Interpretation of a good overfitting score As shown below, my deep neural network is overfitting :



where the blue lines is the metrics obtained with training set and red lines with validation set

Is there anything I can infer from the fact that the accuracy on the training sets is really high (almost 1) ?

From what I understand, it means that the complexity of my model is enough / too big. But does it means my model could theoretically reach such a score on validation set with same dataset and appropriate hyperparameters ? With same hyperparameters but bigger dataset ? 

My question is not how to avoid overfitting.
It doesn't tell you very much, to be honest. It does mean that (assuming your training and validation distributions are similar) your model could get the same results on your validation set should you train on that, but that would still be overfitting.

Really, the only useful thing overfitting tells you is that you don't have enough regularisation.