# Is discriminator a regressor or classifier in implementations?

GAN has two components: generator and discriminator.

Discriminator in the original GAN is a regressor and always gives value in $$[0, 1]$$. You can read it in original paper

$$D(x)$$ represents the probability that $$x$$ came from the data rather than $$p_g$$

Is it true with most of the (advanced or) contemporary GANs? Or the do nature of discriminator, either as a regressor or as a classifier entirely depends on the context?