Image impainting using deep neural networks
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 inpain...
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| Format: | master thesis |
| Publication Date: | 2023 |
| Country: | España |
| Institution: | Universitat Politècnica de Catalunya (UPC) |
| Repository: | UPCommons. Portal del coneixement obert de la UPC |
| Language: | English |
| OAI Identifier: | oai:upcommons.upc.edu:2117/388362 |
| Online Access: | https://hdl.handle.net/2117/388362 |
| Access Level: | Open access |
| Keyword: | Neural networks (Computer science) Deep learning Image processing Image impainting Neural networks Machine learning Self-supervised neural networks Generative adversarial networks Xarxes neuronals (Informàtica) Aprenentatge profund Àrees temàtiques de la UPC::Enginyeria de la telecomunicació |
| Summary: | 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. |
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