The Future of Transfer Learning in Computer Vision

There are a couple of broad categories or approaches to transfer learning. One is kind of fine-tuning chop off layers starting from the head and work your way back and fine-tune the other is kind of keep things relatively static and access internals via probes. The research dominated by approaches that fall into one of those two categories or is it broader than thatYeah so I think for computer vision right now this is a fairly common approach to transfer learning and the area of NLP. With the rise of large language models as a as a way of capturing a lot of background information about language about code also and many more things another approach that's been very popular is has been prompting.