I can’t tell if you want an actual answer or an ELI5.

Actual: various detection tasks, as compared to other image networks.

Simple version: Many nn tasks work by feeding a lot of data into a network, then “refining” it with a task you want it to actually do.

So let’s say I want to detect corgis.

I take a network that can detect all kinds of shit, and feed it a bunch of images of dogs and tell it, no, only these ones please.

Why?

…because you don’t have 10TB of dog images, you have 10TB of pictures of random crap, and 1GB of corgi images.

…and fair intuitively, this works. If something knows how to tell dogs from cats from cars, it’s not a big stretch to become more specific and only detect corgis.

Now, this paper is showing that instead of feeding the initial network labelled images of cats, dogs, etc … you just feed it with procedural noise, it still works.

That’s a) surprising (wtf, why does telling the difference between a squiggly and another squiggly help you tel corgis from cats?) …

And b) really important, because it means you don’t need to spend hundreds (thousands) of hours or $$$ collecting the initial datasets.

Practically, what does that mean?

Well, here’s some food for thought: Google / Amazon etc are considered to have a fairly defensible moat for their voice recognition tech, because they are the only ones with enough data to train good models.

…but that moat vanishes in a blinding flash of steam if you can get comparable results from just feeding enough generated noise into a network.

So this is pretty interesting stuff.