Re:PassGAN: A Deep Learning Approach for Password Guessing
by Cacti on Tuesday September 19, 2017 @10:08AM
(#15284708)
The generator is passed random noise from a distribution, so iterating through all the samples is the same as just sampling from that distribution a ton.
If your noise is uniform on [0, 1], then, sure, you could just iterate through every possible float, I guess. Though in practice you're talking an astronomical number of possible combinations.
Another option is to take two samples from the distribution and interpolate in-between them, which might give you an idea of the distribution of samples. Maybe, it depends on the GAN and data.
If your noise is uniform on [0, 1], then, sure, you could just iterate through every possible float, I guess. Though in practice you're talking an astronomical number of possible combinations.
Another option is to take two samples from the distribution and interpolate in-between them, which might give you an idea of the distribution of samples. Maybe, it depends on the GAN and data.