Improving the quality of image generation in art with top-k training and cyclic generative methods Article Swipe
Laura Alejandra Granados Vela
,
Félix Fuentes-Hurtado
,
Adrián Colomer
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-023-44289-y
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-023-44289-y
The creation of artistic images through the use of Artificial Intelligence is an area that has been gaining interest in recent years. In particular, the ability of Neural Networks to separate and subsequently recombine the style of different images, generating a new artistic image with the desired style, has been a source of study and attraction for the academic and industrial community. This work addresses the challenge of generating artistic images that are framed in the style of pictorial Impressionism and, specifically, that imitate the style of one of its greatest exponents, the painter Claude Monet. After having analysed several theoretical approaches, the Cycle Generative Adversarial Networks are chosen as base model. From this point, a new training methodology which has not been applied to cyclical systems so far, the top- k approach, is implemented. The proposed system is characterised by using in each iteration of the training those k images that, in the previous iteration, have been able to better imitate the artist’s style. To evaluate the performance of the proposed methods, the results obtained with both methodologies, basic and top- k , have been analysed from both a quantitative and qualitative perspective. Both evaluation methods demonstrate that the proposed top- k approach recreates the author’s style in a more successful manner and, at the same time, also demonstrate the ability of Artificial Intelligence to generate something as creative as impressionist paintings.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41598-023-44289-y
- https://www.nature.com/articles/s41598-023-44289-y.pdf
- OA Status
- gold
- Cited By
- 6
- References
- 37
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387739094
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4387739094Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1038/s41598-023-44289-yDigital Object Identifier
- Title
-
Improving the quality of image generation in art with top-k training and cyclic generative methodsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-10-18Full publication date if available
- Authors
-
Laura Alejandra Granados Vela, Félix Fuentes-Hurtado, Adrián ColomerList of authors in order
- Landing page
-
https://doi.org/10.1038/s41598-023-44289-yPublisher landing page
- PDF URL
-
https://www.nature.com/articles/s41598-023-44289-y.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.nature.com/articles/s41598-023-44289-y.pdfDirect OA link when available
- Concepts
-
Generative grammar, Style (visual arts), Computer science, Perspective (graphical), Artificial intelligence, Quality (philosophy), Painting, Point (geometry), Image (mathematics), Adversarial system, Artificial neural network, Visual arts, Mathematics, Art, Epistemology, Geometry, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 3Per-year citation counts (last 5 years)
- References (count)
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37Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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