The Role of Pre-Trained Learning in Transfer Learning

I think there's an idea that doing unsupervised learning is going to capture something more informative about the structure of the data and those features were essentially basically good and they just needed to be adjusted a little bit. So some of my work with people here in my group LNE, Trian Tefilu in particular we've kind of explored this idea of having kind of a would call that a template which would be the pre-trained model for each downstream task. And how do you characterize the research landscape around this idea that there's more than just fine-tuning to transfer learning? Where are we in terms of the way we think about it?