# Discriminator Rejection Sampling

## 2020/11/20

This note is a summary of the main idea behind Discriminator Rejection Sampling  in GANs.

## 1Rejection sampling

Suppose there is a true data distribution with density $$p_d (x)$$ which is hard to sample from and an easy to sample from “proposal” distribution $$p_g (x)$$. Assume there is an $$M < \infty$$ such that:

$$\displaystyle Mp_g (x) > p_d (x), \quad \forall x \in \operatorname{supp} (p_d)$$

Then we can generate samples according to $$p_d$$ by sampling from $$p_g$$ and then accepting with probability:

 $$\displaystyle \frac{p_d (x)}{Mp_g (x)}$$ (1)

## 2Discriminator rejection sampling for GANs

In a GAN, we can consider the generator as the proposal distribution $$p_g$$, and we are trying to train it to match the true distribution $$p_d$$. The idea behind discriminator rejection sampling is to use rejection sampling to generate samples from $$p_d$$, even if $$p_g$$ doesn't exactly match $$p_d$$. Unfortunately, Equation 1 requires the ratio of two densities which we don't have. In particular, even if we could compute $$p_g (x)$$ it is rare to have the true density $$p_d (x)$$. However, we can aim to estimate the ratio $$\frac{p_d}{p_g}$$ directly using the discriminator.

In a GAN, the discriminator $$D$$ takes an input $$x \sim p_{\operatorname{mix}}$$ which is a balanced mix of the real data distribution $$p_d$$ and the generator distribution $$p_g$$. Its job is to output the probability that $$x$$ is from $$p_d$$, and is trained using the binary classification loss:

$$\displaystyle \max_D \mathbb{E}_{x \sim p_d} [\log D (x)] +\mathbb{E}_{x \sim p_g} [\log (1 - D (x))]$$

Let's assume we can train $$D$$ to be the optimal discriminator. What does that look like? Since $$p_{\operatorname{mix}}$$ is a balanced mix of $$p_d$$ and $$p_g$$, the density is:

$$\displaystyle p_{\operatorname{mix}} (x) = \frac{1}{2} p_d (x) + \frac{1}{2} p_g (x)$$

We want our discriminator to produce, for $$x \sim p_{\operatorname{mix}}$$:

\begin{eqnarray*} D (x) & = & p (\operatorname{real}|x)\\ & = & \frac{p (x|\operatorname{real}) p (\operatorname{real})}{p_{\operatorname{mix}} (x)}\\ & = & \frac{p_d (x) \times \frac{1}{2}}{\frac{1}{2} p_d (x) + \frac{1}{2} p_g (x)}\\ & = & \frac{p_d (x)}{p_d (x) + p_g (x)}\\ & = & \frac{1}{1 + \frac{p_g (x)}{p_d (x)}} \hspace{3cm} \text{(2)} \end{eqnarray*}

Usually, the discriminator's output is the sigmoid of some logits $$\tilde{D}$$:

 $$\displaystyle D (x) = \sigma (\tilde{D} (x)) = \frac{1}{1 + e^{- \tilde{D} (x)}}$$ (3)

Comparing equations 3 and 2 we see that:

$$\displaystyle \frac{p_d (x)}{p_g (x)} = \exp (\tilde{D} (x))$$

This gives us most of what we need to do rejection sampling (Equation 1). Now we want to find a value for $$M$$:

$$\displaystyle M = \max_x \frac{p_d (x)}{p_g (x)} = \max_x \exp (\tilde{D} (x))$$

In practice we sample a bunch of $$x \sim p_g$$ and take the max to get our estimate of $$M$$. Then we can do rejection sampling by sampling $$x \sim p_g$$ and then accepting with probability

$$\displaystyle \frac{\exp (\tilde{D} (x))}{M}$$

## Bibliography

 Samaneh Azadi, Catherine Olsson, Trevor Darrell, Ian Goodfellow, and Augustus Odena. Discriminator rejection sampling. ArXiv preprint arXiv:1810.06758, 2018.