Conditional GANs (CGANs) are an extension of the GANs model. You can read about a variant of GANs called DCGANs in my previous post here. CGANs are allowed to generate images that have certain conditions or attributes.

Like DCGANs, Conditional GANs also have two components.

  • A Generator(An artist) neural network.
  • A Discriminator(An art critic) neural network.

Conditional GANs (CGANs): The Generator and Discriminator both receive some additional conditioning input information. This could be the class of the current image or some other property.

For example, if we train a DCGANs to generate new MNIST images, There is no control over which specific digits will be produced by the Generator. There is no mechanism for how to request a particular digit from the Generator. This problem can be addressed by a variation of GAN called Conditional GAN (CGAN). we could add an additional input layer with values of one-hot-encoded image labels.

In conditional GANs (CGANs)

  • Adding a vector of features controls the output and guide Generator figure out what to do.
  • Such a vector of features should derive from a image which encode the class(like an image of a woman or a man if we are trying to create faces of imaginary actors) or a set of specific characteristics we expect from the image (in case of imaginary actors, it could be the type of hair, eyes or complexion).
  • We can incorporating the information into the images that will be learned and also into the Z input, which is not completely random anymore.
  • Discriminator’s evaluation is done not only on the similarity between fake data and original data but also on the correspondence of the fake data image to its input label (or features)
  • We can using the same DCGANs and imposed a condition on both Generator’s and Discriminator’s inputs. The condition should be in the form of a one-hot vector version of the digit. This is associated with the image to Generator or Discriminator as real or fake.

NOTE: CGANs have the one disadvantage. CGANs are not strictly unsupervised and we need some kind of labels for them to work.

High-Level CGAN’s Architecture Diagram

Conditional GAN

The Discriminator’s Network

The CGAN Discriminator’s model is similar to DCGAN Discriminator’s model except for the one-hot vector, which is used to condition Discriminator outputs. You can read about Discriminator’s Network in my previous post here

The Generator’s Network

The CGAN Generator’s model is similar to DCGAN Generator’s model except for the one-hot vector, which is used to condition Generator outputs. You can read about Generator’s Network in my previous post here

The DCGAN architecture of the generator

CGAN’s Architecture Diagram

CGAN’s Architecture

Loss Functions

The Discriminator has two task

  • Discriminator has to correctly label real images which are coming from training data set as “real”.
  • Discriminator has to correctly label generated images which are coming from Generator as “fake”.

We need to calculate two losses for the Discriminator. The sum of the “fake” image and “real” image loss is the overall Discriminator loss. So the loss function of the Discriminator is aims to minimize the error of predicting real images coming from the dataset and fake images coming from the Generator given their one-hot labels.

Discriminator’s loss function

The Generator network has one task

  • To create an images that looks as “real” as possible to fool the Discriminator.

The loss function of the Generator minimizes the correct prediction of the Discriminator on fake images conditioned on the specified one-hot labels.

Generator’s loss function

Training of DCGANs

The following steps are repeated in training

  • The Discriminator is trained using real and fake data and generated data.
  • After the Discriminator has been trained, both models are trained together.
  • First the Generator creates some new examples.
  • The Discriminator’s weights are frozen, but its gradients are used in the Generator model so that the Generator can update it’s weights.

Discriminator’s Training Flow

Discriminator’s training process

Generator’s Training Flow

Generator’s training process

Accompanied jupyter notebook for this post can be found here.


CGANs can be used to build a model which can generate image of imaginary actor of given class like male or female. It can also use to build Face Aging system, Age synthesis and age progression have many practical industrial and consumer applications like cross-age face recognition, finding lost children, entertainment, visual effects in movies.

I hope this article helped you get started building your own CGANs. I think it will at least provides a good explanation and understanding about CGANs.

Source: Artificial Intelligence on Medium