AttnGAN: Realistic Text-to-Image Synthesis with Attentional Generative Adversarial Networks.

In this paper, we propose a prototype design for manifold refinement to fine grained text-to-image generation by using Attentional Generative Adversarial Network (AttnGAN) We concentrate on creating realistic images from text descriptions. We have used a collection of Attentional Generative Adversarial Network layers that are able to correctly select the modal meaning at the word-level and sentence-level. Generative Adversarial Networks (GANs) prove to be fundamental structure for many design applications from Game design, Art, Science and Modelling applications. We use GANs for contrastive learning and as a information maximisation approach, and we do extensive research to find the further advancements in image generation. Our prototype is easy to implement and practical; choosing the most relevant word vectors and using those vectors to generate related image sub-regions. The prototype in its current state generates image designs only for the bird species to satisfy the claim for its image generation ability. With due consideration to findings of usability testing, the develpment team in future iterations of the application, hopes to improve the generated image resolution. They plan to provide a choice for created variety of images with further improvements to the image generation algorithm.