Machine Learning Projects for Mobile Applications

Steps in GAN

Based on our example, assume that we are passing numbers as inputs:

1. The generator takes random numbers as inputs and returns an image as the output.
2. The output image is passed into the discriminator and at the same time the discriminator receives input from the dataset as well.
3. The discriminator takes in both real and fake input images and returns probabilities between 0 and 1, with 1 representing a prediction of authenticity and 0 representing fake.

We have represented the same in our application by passing the user hand-drawn image as one of the fake images and try to get the probability value of it.