You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
{{ message }}
This repository has been archived by the owner on Feb 28, 2018. It is now read-only.
One of the key nice things about neural networks is that they are resilient to some damage.
That means either damage to the network itself (e.g. broken links), or .. more interestingly .. damage to the input image.
It might be interesting for users to draw a digit, then click "damage" and "distort" buttons which would blow holes in the image, or stretch/wobble/twist the image a bit.
One of the key nice things about neural networks is that they are resilient to some damage.
That means either damage to the network itself (e.g. broken links), or .. more interestingly .. damage to the input image.
It might be interesting for users to draw a digit, then click "damage" and "distort" buttons which would blow holes in the image, or stretch/wobble/twist the image a bit.
This would allow users to see for themselves that you can damage an input image quite a bit before overall performance degrades. I did some experiments myself at http://makeyourownneuralnetwork.blogspot.co.uk/2016/03/your-own-handwriting-real-test.html
The text was updated successfully, but these errors were encountered: