@MASTERSTHESIS{ 2019:208812283,
 	title = {Style transfer for text-based image manipulation},
 	year = {2019},
 	url = "http://tede2.pucrs.br/tede2/handle/tede/8983",
 	abstract = "A large amount of the data we produce nowadays is in the form of digital photographs, which increases the demand for photo editing applications. However, image manipulation has a steep learning curve; as such, it would be invaluable to automate or simplify this artistic process to make it more accessible. In this study, we investigate the use of a subset of natural language (more specifically, textual descriptions of objects) as input to automatize image manipulation. We propose a deep learning approach for the task of textbased image manipulation that combines adversarial learning and style transfer concepts. We evaluate our method, compare it to baseline approaches, and conclude that our results have competitive quality when compared to the current state-of-the-art.",
 	publisher = {Pontif?cia Universidade Cat?lica do Rio Grande do Sul},
 	scholl = {Programa de P?s-Gradua??o em Ci?ncia da Computa??o},
 	note = {Escola Polit?cnica}
}