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by std_throwaway
3261 days ago
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> I am unsure what you mean-- do you mean with different training sets but the same testing set? Yes, assuming we have 10000 different training images. Divide these into 5 sets of 2000 each and train 5 networks with them. Assuming that 2000 images are plenty for this application, we will have 5 well trained networks that have similar performance for a test set. BUT They will work slightly differently internally and those "inverse gradient search" methods (or what they are called) might only be able to manipulate an image for one network at the time with "specifically chosen additive noise" while the other 4 are unimpressed. That's assuming that the manipulation can't be targeted at all 5 classifiers at the same time. |
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