# What is the meaning of $p_{\text {data }}(y)$ in the CycleGAN?

In the original CycleGAN paper, on the second page, there is a sentence that I didn't quite understand

In theory, this objective can induce an output distribution over $$\hat{y}$$ that matches the empirical distribution $$p_{\text {data }}(y)$$ (in general, this requires $$G$$ to be stochastic) [16].

What does $$p_{\text {data }}(y)$$ denote? Furthermore, I can't imagine the empirical distribution of it.

In the loss functions, there is also $$x \sim p_{\text {data }}(x)$$, but I also don't get the context there.

Could anyone please elaborate further and explain this sentence to me?

I interpret $$p_{data}(y)$$ as the empirical probability of seeing an image $$y$$ in the training data.
For example, in a typical training run, each training image is shown to the network the same number of times, so $$p_{data}(y)$$ is a discrete distribution with constant probability $$p_{data}(y) = \frac 1 N$$. Thus, in this case:
In theory, this objective can induce an output distribution over $$\hat y$$ that matches the empirical distribution $$p_{data}(y)$$.
means that training $$G$$ to minimize this objective can result in a function $$G$$ such that, if you first choose a random image $$x \in X$$, then calculate $$G(x)$$, the probability of obtaining any particular output image $$y$$ will also be $$\frac 1 N$$. That is:
$$p(\hat y = y) = E_x[p(G(x) = y)] = \frac 1 N$$
• Your response is absolutely right. I would only like to remark that usually people don't actually compute $p_{data}(y) = 1/N$. Rather, they sample $y \sim p_{data}$ which corresponds to selecting a random batch from your dataset. This suffices, for instance, for computing averages of functions over y. Jun 10 at 15:01