  The field of image generation through generative modelling is abundantly
discussed nowadays. It can be used for various applications, such as up-scaling
existing images, creating non-existing objects, such as interior design scenes,
products or even human faces, and achieving transfer-learning processes. In
this context, Generative Adversarial Networks (GANs) are a class of widely
studied machine learning frameworks first appearing in the paper "Generative
adversarial nets" by Goodfellow et al. that achieve the goal above. In our
work, we reproduce and evaluate a novel variation of the original GAN network,
the GANformer, proposed in "Generative Adversarial Transformers" by Hudson and
Zitnick. This project aimed to recreate the methods presented in this paper to
reproduce the original results and comment on the authors' claims. Due to
resources and time limitations, we had to constrain the network's training
times, dataset types, and sizes. Our research successfully recreated both
variations of the proposed GANformer model and found differences between the
authors' and our results. Moreover, discrepancies between the publication
methodology and the one implemented, made available in the code, allowed us to
study two undisclosed variations of the presented procedures.
