So far, we have implemented different GAN networks for different use cases. Conditional GANs extend the idea of vanilla GANs and allow us to control the output of the generator network. Face aging is all about changing the age of a person's face without changing their identity. In most other models (including GANs), the appearance or identity of a person is lost by 50% because facial expressions and facial accessories, such as sunglasses or beards, are not taken into account. Age-cGANs consider all of these attributes. In this section, we will explore cGANs for face aging.