From Many Neural Networks to One Faster Model
Computers often do better when lots of models vote together, but using the whole team is slow and expensive.
A clever idea is to teach one model to copy the team's answers so it acts like the group.
This team of models can be squeezed into a single smaller model, so phones and apps can run it faster, for many people.
It makes things like handwriting and speech work better while using less power, with better accuracy in many cases.
Sometimes big models get confused by very similar things, so small extra helpers — called specialist models — learn just those tricky bits, and they train quick and side-by-side.
The result is almost the same smarts as a big group, but easier and cheaper to send to millions of users.
Think of it like teaching one student the class answers, plus a few tutors for hard questions.
Apps get smarter, run quicker, and cost less to use, so everyday tech feels more helpful and smooth.
Read article comprehensive review in Paperium.net:
Distilling the Knowledge in a Neural Network
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