Good question; I do not have a source for that, have just heard colleagues saying that. Obviously the reason for freezing layers is that we are trying to avoid loosing all the information we have already gained. Should speed up further training by reducing parameter numbers etc. As to actually WHICH layers are best persevered I don’t know. When I have read on it, people typically say “it depends”. But actually my point was I have never found transfer learning to be terribly effective (apart from years ago when I ran a specific transfer learning tutorial!). In my models it only takes a few days to start from scratch and so this it what I do! Transfer learning obviously makes enormous sense if you are working with someone else’s extravagantly trained model and you may be don’t even have the data. But in my case I always do have all the data…