Abstract:
Generative Adversarial Networks that fall under the class of generative models, aim at taking
training examples from the training set and learning the probability distribution that generates
those samples. The network is adversarial in the sense that the discriminator tries to maximize
the probability of identifying the real data whereas the generator tries to fool the discriminator by
producing synthetic data as close as possible to the ground truth value. In recent years, many
powerful models using neural network architectures have been introduced that try to learn the
discriminative features of the text representations. Also, GANs have been extremely successful in
generating realistic images belonging to various categories. Influenced by the success of GANs,
researchers thought of applying the GAN model in the human face synthesis task. There exist
several attempts for the face synthesis task that try to generate real human faces from the form of
input given to them . Unlike the already existing attempts to create human faces, our model tries
to apply the concept of text-to-image synthesis [1] GAN in the generation of human faces from
the text description stating the attributes o of their respective faces provided as the input. The
training of Generator is assisted by the adversary Discriminator (Matching-aware Discriminator
model(CNN)) that differentiates the results given by the Generator and the ground truth values.
The Generator model would thus learn to generate the human faces that are similar to the ground
truth values and thus try to cheat the adversary. The aim is to produce strong results and see the
behavior of GAN model in the human face generation task.
