Question about KL computation

[SerezD]

In distributions.py, the KL is computed as indicated in section 3.2 of the paper (residual normal distributions, Equation 2):

def kl(self, normal_dist):
    term1 = (self.mu - normal_dist.mu) / normal_dist.sigma
    term2 = self.sigma / normal_dist.sigma

    return 0.5 * (term1 * term1 + term2 * term2) - 0.5 - torch.log(term2) 

What I don't understand is, why you compute term2 = self.sigma / normal_dist.sigma. Shouldn't it be:
term2 = self.sigma - normal_dist.sigma?

[SerezD]

Nevermind, I got it:

checking section 3.2 of the paper, paragraph: Residual Normal Distributions.

In the code self.mu, self.sigma are the parameters of the posterior distribution
prior.mu, prior.sigma are the parameters of the prior distribution

p(z_i|z_{l<i}) is the prior, defined as N(μ_p, σ_p) where both params are conditioned on all z_{l<i}

q(z_i|z_{l<i}, x) is the distribution from the encoder (self), defined as:

q = N(μ_p + Δμ_q, σ_p * Δσ_q), where Δμ_q, Δσ_q are the relative shift and scale given by the hierarchical nature of the distribution.

So basically, self.mu and self.sigma are the parameters of the posterior:

self.mu = μ_p + Δμ_q
self.sigma = σ_p * Δσ_q

The KL Loss between two normal distributions a = N(μ_1, σ_1), b = N(μ_2, σ_2) is given by:

 0.5 [ (μ_2 - μ_1)**2 / σ_2**2 ] + 0.5 (σ_1**2 / σ_2**2) - 0.5 [ln(σ_1**2 / σ_2**2)] - 0.5

proof: https://statproofbook.github.io/P/norm-kl.html
In our case: μ_1 = self.mu; μ_2 = prior.mu; σ_1 = self.sigma; σ_2 = prior.sigma

So the three terms in the formula above become:
1. 0.5 [ (μ_p - μ_p + Δμ_q)**2 / σ_p**2] = 0.5 [ Δμ_q**2 / σ_p**2]
2. 0.5 ((σ_p * Δσ_q)**2 / σ_p**2) = 0.5 [Δσ_q**2]
3. 0.5 [ln((σ_p * Δσ_q)**2 / σ_p**2)] = 0.5 ln(Δσ_q**2)

The final formula is thus the one written in Equation 2 and (in the code):
Δμ_q = self.mu - prior.mu
Δσ_q = self.sigma / prior.sigma