Text to Image Synthesis Using Multistage Stack GAN

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Author(s): V. Dinesh Reddy (SRM University, India), Yasaswini Desu (SRM University, India), Medarametla Sindhu (SRM University, India), Chilukuri Vamsee (SRM University, India)and Neelissetti Girish (SRM University, India)
Copyright: 2023
Pages: 16
Source title: Handbook of Research on AI Methods and Applications in Computer Engineering
Source Author(s)/Editor(s): Sanaa Kaddoura (Zayed University, UAE)
DOI: 10.4018/978-1-6684-6937-8.ch010

Keywords: Artificial Intelligence / Computer Engineering / Computer Science & IT / Engineering Science Reference

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Abstract

Many recent studies on text-to-image synthesis decipher approximately 50% of the problem only. They failed to compute all the imperative details in it. This chapter presents a solution using stacked generative adversarial networks (GAN) to generate lifelike images based on the given text. The stage-I GAN creates a distorted images by depicting the rudimentary/basic colours and shape of a scene predicted on text illustration. Stage-II GAN ends up on generating high-resolution images with naturalistic features using Stage-I findings and the text description as inputs. The output generated by this technique is more credible than many other techniques which are already in use. More importantly, stack GAN produces 256 x 256 images based on the text descriptions, while the existing algorithms produces 128 x 128.

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