3 ms·
They're not changing the original network. That would not be very interesting. They're generating objects that fool the correctly trained network.
by readams 9y ago
They're not changing the original network. That would not be very interesting. They're generating objects that fool the correctly trained network.
- jon_richards 9y ago>given access to the classifier gradient, we can make adversarial examples It seems like they are finding little "inflection points" in the trained network where a small, well-placed change of input can flip the result to something different. With the rise of "AI for AI", I imagine this is something that could be optimized against. In the turtle example, it seems that google's classifier has found that looking for a couple specific things (mostly a trigger in this case) identifies a gun better than looking for the entirety of a gun. Perhaps optimizing against these inflection points will force the classifier to have a better understanding of the objects it is classifying and lead to better results in non-adversarial situations.