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@Imperssonator I think both the lines are equivalent.
Notice that since epsilon is drawn from a uniform distribution, you can equivalently replace epsilon by (1-epsilon), both of these expressions would be equivalent.
Now note that if you replace epsilon by (1-epsilon) in the paper's definition, you would get the one in the code. So both are correct.
Line 76 of train_wgan_GP:
X_hat = X_real + epsilon * (X_fake - X_real)
From the paper this should be (Algorithm 1 Line 6):
X_hat = epsilon * X_real + ( 1 - epsilon ) * X_fake
In the format you used above:
X_hat = X_fake + epsilon * (X_real - X_fake)
In other words, X_hat should be a small nudging of X_fake toward X_real, not a small nudging of X_real toward X_fake...
Let me know if I'm missing something or if there's a reason it was done this way.
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