I am trying to compute the Inception core for my GAN network.
but i have observed something very interesting. Why are we using only
X for the calculation i taught KL divergence was suppose to use two variables main distribution
P(x) and predicted Model
def inception_score(X): kl = X * ((X + eps).log() - (X.mean(0) + eps).log().expand_as(X)) score = np.exp(kl.sum(1).mean()) return score
but it looks like the KL divergence is between
X and its mean
X.mean(0) i.e if
X is the the generated image, then what’s actually going on?