Text to Image Translation using Cycle GAN

Abstract

In the recent past, text-to-image translation was an active field of research. The ability of a network to know a sentence's context and to create a specific picture that represents the sentence demonstrates the model's ability to think more like humans. Common text--translation methods employ Generative Adversarial Networks to generate high-text-images, but the images produced do not always represent the meaning of the phrase provided to the model as input. Using a captioning network to caption generated images, we tackle this problem and exploit the gap between ground truth captions and generated captions to further enhance the network. We present detailed similarities between our system and the methods already in place. Text-to-Image synthesis is a difficult problem with plenty of space for progress despite the current state-of - the-art results. Synthesized images from current methods give the described image a rough sketch but do not capture the true essence of what the text describes. The re-penny achievement of Generative Adversarial Networks (GANs) demonstrates that they are a decent contender for the decision of design to move toward this issue.

1D-DGAN-PHM: A 1-D denoising GAN for Prognostics and Health Management with an application to turbofan

“…A model that has shown quite promise in a wide range of tasks is the Generative Adversarial Networks (GANs). Originally proposed by Goodfellow et al [12], who outlined the learning theory of GANs on a game theoretic scenario, these networks have since shown remarkable capability in diverse domains, such as image generation [64][65][66][67], text to image translation [68,69], and image to image translation [70][71][72]. In simple terms, a GAN consists of two networks: a generator and a discriminator.…”