Conditional Generative Models
A conditional generative model generates data conditioned on additional information, modeling p(x|y) instead of p(x), which allows controlling what is produced. We explain its contrast with unconditional models, how it is implemented in GANs, variational autoencoders and diffusion, and its uses.
A conditional generative model is one that learns to generate data conditioned on additional information—a condition or label—modeling the distribution p(x|y) instead of the general distribution p(x). That condition can be a class, a text, another image or a map, and it serves to control what is generated, rather than sampling blindly.
Conditional versus unconditional
An unconditional generative model produces some plausible example of the domain, but does not allow asking which; it samples from p(x) without control. The conditional one adds the condition to the input of the generative process and thereby directs the output: instead of any digit, it generates “a 7”; instead of an arbitrary image, it generates an image “from a text.” In exchange, it requires well-prepared data and condition signals.
How it is implemented
The conditional GAN feeds the condition to both generator and discriminator: it is a concrete example of directing the generated distribution.
What it is used for
Conditional generative models are behind text-to-image generation, image-to-image translation (colorizing a photo, turning a sketch into an image), speech synthesis and targeted data augmentation, which generates examples of a specific class to balance a dataset. Primary source.
This article was produced with artificial intelligence under human editorial oversight.
