Image impainting using deep neural networks
Tutor / Supervisor
Student
Zurita Montes De Oca, Erika Anabel
Document type
Master thesis
Date
2023
rights
Open Access
Publisher
Universitat Politècnica de Catalunya
UPCommons
Abstract
Nowadays, virtual markets are increasingly available and seek to connect shoppers with products. Due to the high turnover of products in a physical market, it is very likely to find relevant differences between products in the physical and digital markets. This paper proposes the use of image inpainting using deep neural networks to solve this problem. It is proposed to use the approach performed by [1] based on generative adversarial networks as they are one of the most inventive and promising architectures. Through the experiments performed, it has been possible to prove that using this method it is possible to train models that produce realistic terminations of products that have been eliminated or that are to be replaced. We have also made a comparison with another interesting approach that had shown good results in the task of content generation in arbitrary zones.
