  Text to Image Synthesis refers to the process of automatic generation of a
photo-realistic image starting from a given text and is revolutionizing many
real-world applications. In order to perform such process it is necessary to
exploit datasets containing captioned images, meaning that each image is
associated with one (or more) captions describing it. Despite the abundance of
uncaptioned images datasets, the number of captioned datasets is limited. To
address this issue, in this paper we propose an approach capable of generating
images starting from a given text using conditional GANs trained on uncaptioned
images dataset. In particular, uncaptioned images are fed to an Image
Captioning Module to generate the descriptions. Then, the GAN Module is trained
on both the input image and the machine-generated caption. To evaluate the
results, the performance of our solution is compared with the results obtained
by the unconditional GAN. For the experiments, we chose to use the uncaptioned
dataset LSUN bedroom. The results obtained in our study are preliminary but
still promising.
