The question is, is the power of deep nets constrained to tasks where you have a huge amount of data.
there are a large amount of applications where you can use big models trained on generic tasks where the data is free and available to everyone, and then fine tune to your specific more limited data.
let's say I need a person detector for my cctv camera. I use a pretrained ResNet ImageNet model distributed by Google, that was trained on a super large dataset identifying things like animals, furniture and whatnot on a huge free dataset, and then train it just a bit more on my specific task.
this allows to create very performant networks with very little task specific data.
there are a large amount of applications where you can use big models trained on generic tasks where the data is free and available to everyone, and then fine tune to your specific more limited data.
let's say I need a person detector for my cctv camera. I use a pretrained ResNet ImageNet model distributed by Google, that was trained on a super large dataset identifying things like animals, furniture and whatnot on a huge free dataset, and then train it just a bit more on my specific task.
this allows to create very performant networks with very little task specific data.