Thanks for your answer.
Just to clarify, I did not mean to keep the model which was already fine tuned with 80% of the data and do some further fine tuning only with the remaining 20%. I meant use the exact same hyper-parameters I have chosen after doing the standard validation process (including number of epochs), and then retrain everything from scratch with those same hyper parameters but with 100% of the data, so I’m not sure if your first point is fully relevant here.
On your second point I agree with you, but as I said, if I use exactly the same hyper parameters which worked best when training with 80% of the data to retrain with 100% of the data from scratch, it’s unlikely that the performance will be worse than what I got with 80% of the data, so I will still know that it’s at least equal if not better than what I got before (although I won’t know how much better), but this would still be enough for me.
Does anyone have further comments based on this clarification?