Reparameterization Trick in VAE I was reading this web page on variational auto-encoders, and am unable to understand how the function below is generated. Based on my limited understanding, the sampling part of the VAE which uses a gaussian distribution cannot be backprop-ed. So we are forced to re-write the equation.
The part I do not understand is how we are able to write the gaussian equation
$$
\frac{1}{ \sqrt{2\pi\sigma^2}} e^{\frac{-(x-\mu)^2}{2\sigma^2}}
$$
into the things written below. If someone has a link to the proof or the derivation please post it here. Or if I have totally missed the point please kindly explain :)
This reparametrization is possible because of two properties of Gaussian random variables. I'll stick to the 1D case for simplicity:
1. If $\mathbf{X \sim N(\mu, \sigma^2)}$ and $\mathbf{\alpha \in \mathbb{R}}$ then $\mathbf{\alpha X \sim N(\alpha\mu, \alpha^2\sigma^2)}$.
Proof: If $\alpha = 0$ it is trivial. Suppose that $\alpha \neq 0$. We have
$$P(\alpha X \leq t) = P(X \leq t/\alpha) = \frac{1}{\sqrt{2\pi}\sigma}\int_{-\infty}^{t/\alpha} e^{-\frac{(x-\mu)^2}{2\sigma^2}} dx$$
Setting $u = \alpha x$ we have
$$\frac{1}{\sqrt{2\pi}\sigma}\int_{-\infty}^{t/\alpha} e^{-\frac{(x-\mu)^2}{2\sigma^2}} dx = \frac{1}{\sqrt{2\pi}\sigma}\int_{-\infty}^{t} e^{-\frac{(u-\alpha\mu)^2}{2\alpha^2\sigma^2}} \frac{1}{\alpha}du = F_{N(\alpha\mu, \alpha^2\sigma^2)}(t)$$
That is, the cumulative distribution function of $\alpha X$ is that of a Gaussian $N(\alpha\mu, \alpha^2\sigma^2)$, so it is the case that $X \sim N(\alpha\mu, \alpha^2\sigma^2)\quad\blacksquare$
2. If $\mathbf{X \sim N(\mu, \sigma^2)}$ and $\mathbf{\beta \in \mathbb{R}}$ then $\mathbf{\beta + X \sim N(\beta + \mu, \sigma^2)}$.
Proof: As before,
$$P(\beta + X \leq t) = P(X \leq t - \beta) = \frac{1}{\sqrt{2\pi}\sigma}\int_{-\infty}^{t-\beta} e^{-\frac{(x-\mu)^2}{2\sigma^2}} dx$$
Setting $u = x + \beta$:
$$\frac{1}{\sqrt{2\pi}\sigma}\int_{-\infty}^{t-\beta} e^{-\frac{(x-\mu)^2}{2\sigma^2}} dx = \frac{1}{\sqrt{2\pi}\sigma}\int_{-\infty}^{t} e^{-\frac{(u-\beta-\mu)^2}{2\sigma^2}} du = F_{N(\beta+\mu,\sigma^2)}(t) \quad\blacksquare$$
Consequence:
What (1) and (2) tell you is that for a Gaussian $X \sim N(\mu, \sigma^2)$ it holds $X - \mu \sim N(0, \sigma^2)$ and furthermore $\frac{X-\mu}{\sigma} \sim N(0, 1)$. Equivalently, if $X \sim N(0, 1)$ it holds $\sigma X \sim N(0, \sigma^2)$ and furthermore $\mu + \sigma X \sim N(\mu, \sigma^2)$. That is the reparametrization trick. Now, as others have stated, this is convenient in the context of VAEs because $\mu$ and $\sigma$ may depend on learnable parameters, and with this expression these are decoupled from the sampling process—you sample from the r.v. $N(0, 1)$.
In a VAE, we have an encoder distribution (approximate posterior) $q_\phi(z|x)$. It works by defining two functions $\mu_\phi(x)\in\mathbb{R}^n$, $\Sigma_\phi(x)\in\mathbb{R}^{n\times n}_\text{diag}$ and sampling from the latent space via: $$ z \sim \mathcal{N}(\mu_\phi(x),\Sigma_\phi(x)) $$
The problem is that this sampling operation is not differentiable. E.g. $\partial_\phi\mathcal{L}$ requires one to compute the dependence of $z$ on $\phi$, which is a stochastic function.

To get around this, we can instead write: $$ z = \mu_\phi(x) + \xi \sqrt{\Sigma_\phi(x)} $$ where $\xi\sim \mathcal{N}(0,I)$. Notice that now $z$ depends on $\phi$ deterministically, so we can compute the backprop derivatives, because the non-differentiable sampling operation has been moved "off to the side" of the computation graph.