Projecte llegit
Títol: Image impainting using deep neural networks
Estudiants que han llegit aquest projecte:
- ZURITA MONTES DE OCA, ERIKA ANABEL (data lectura: 12-04-2023)
- Cerca aquest projecte a Bibliotècnica
Director/a: TARRÉS RUIZ, FRANCESC
Departament: TSC
Títol: Image impainting using deep neural networks
Data inici oferta: 26-01-2023 Data finalització oferta: 26-09-2023
Estudis d'assignació del projecte:
- MU MASTEAM 2015
Tipus: Individual | |
Lloc de realització: EETAC | |
Paraules clau: | |
image processing, image impainting, neural networks, machine learning, self-supervised neural networks, generative adversarial networks | |
Descripció del contingut i pla d'activitats: | |
The project will study and implement a deep learning strategy for implementing image
impainting, that is, create blocks of missing information in images. The system will be based on a generative adversarial model and trained using a selfsupervised strategy. The student will analyze different implementations and implement one of them using Pytorch or Keras. |
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Overview (resum en anglès): | |
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. |