[qxcv]

Google Trends can tell you a lot of things :)

https://www.google.com/trends/explore?date=2014-01-01%202017...

More seriously though: others have pointed out that finetuning is pretty popular in some subfields, but it's just one hammer in a of a whole toolbox of techniques which are necessary to make neural nets train (even when you have a tonne of data). Standardisation, choice of initialisation, and choice of learning rate schedule all come to mind as other factors which seem simple, but which can have a huge impact in practice.

Of course, each tool has its limitations. The most obvious limitation of finetuning is that you need a network that's already been trained on vaguely similar data. Pretraining on ImageNet is probably not going to help you solve problems where the size of objects matters, for example, because most ImageNet performance tends to benefit from scale invariance.

I wish you luck with nanonets.ai, but I think it's irresponsible to market this as the "1 weird trick" to bring data efficiency to neural nets.