What is one commonly used variant of GANs?

Conditional GANs (cGANs) are a widely used variant of Generative Adversarial Networks that extend the original GAN framework by conditioning the generation process on additional information. This conditioning can take various forms, such as labels or data from other modalities, which guides the generator in producing outputs that are associated with specific attributes or categories.

For instance, in image generation tasks, a cGAN can be used to generate images of specific classes by providing the model with a label indicating the desired class during the training process. This allows for more control and specificity in the types of samples generated, leading to improved relevance and quality of the outputs.

The development of cGANs has significantly enhanced the capabilities of GANs in applications such as image-to-image translation, text-to-image synthesis, and other tasks where additional information can influence the generative process directly. The ability to tailor the output based on conditioning information has made cGANs a popular choice in many practical applications within the field of generative modeling.