Comments (4)
The internal algorithm is built on input data between 0 and 1([0, 1]
), so if you used normalization during training, we need to reduce it to the 0 to 1 interval for the internal algorithm to process it. There is actually no ambiguity here.
The first principle of program design is to have a unified input and output.
So the internal algorithm here defaults to a unified input of data in the range of 0 to 1, and there is no other meaning.
from adversarial-attacks-pytorch.
I understand, but what about my MNIST example? The input data is already between 0 and 1, but I still normalize it, as it is common to do so.
from adversarial-attacks-pytorch.
If you plan to use normalization in your code, just use it. I don't know what's the problem here?
from adversarial-attacks-pytorch.
I don't understand why, in the case of MNIST, which contains [0,1] values before normalization, we need to use 'set_normalization_used' when we normalize the data. Why can't we provide the attack algorithm with the same data we used for training without using 'set_normalization_used'?
from adversarial-attacks-pytorch.
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