to be fine-tuned a bit and that's an end and passes on high-quality feature vectors again to RNN which learns the temporal dependencies and outputs the predictions what you gain from this is you have a significantly smaller model because you do not need to train the CNN from scratch this means you can have many of the parameters frozen and you end up with a more with a model with trainable parameters going from a hundred thousand before to ten or tens of thousands now which is a lot better suited for little labelled data so again