Text-to-image generation based on AttnDM-GAN and DMAttn-GAN: applications and challenges

Autor:	Razan Bayoumi, Marco Alfonse, Mohamed Roushdy, Abdel-Badeeh M. Salem
Rok vydání:	2023
Předmět:	Control and Optimization Computer Networks and Communications Hardware and Architecture Control and Systems Engineering Computer Science (miscellaneous) Electrical and Electronic Engineering Instrumentation Information Systems
Zdroj:	Bulletin of Electrical Engineering and Informatics. 12:1180-1188
ISSN:	2302-9285 2089-3191
DOI:	10.11591/eei.v12i2.4199
Popis:	The deep fake faces generation using generative adversarial networks (GANs) has reached an incredible level of realism where people can’t differentiate the real from the fake. Text-to-face is a very challenging task compared to other text-to-image syntheses because of the detailed, precise, and complex nature of the human faces in addition to the textual description details. Providing an accurate realistic text-to-image model can be useful for many applications such as criminal identification where the model will be acting as the forensic artist. This paper presents text-to-image generation based on attention dynamic memory (AttnDM-GAN) and dynamic memory attention (DMAttn-GAN) that are applied to different datasets with an analysis that shows the different complexity of different datasets’ categories, the quality of the datasets, and their effect on the results of the resolution and consistency of the generated images.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::3a27dac4a0c46b5633986356e0255758 https://doi.org/10.11591/eei.v12i2.4199 Zobrazit plný text záznamu