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Neural Style Transfer for Picture with Gradient Gram Matrix Description
- Source :
- 2020 39th Chinese Control Conference (CCC), 2020 39th Chinese Control Conference (CCC), Jul 2020, Shenyang, China. pp.7026-7030, ⟨10.23919/CCC50068.2020.9188652⟩
- Publication Year :
- 2020
- Publisher :
- HAL CCSD, 2020.
-
Abstract
- International audience; Despite the high performance of neural style transfer on stylized pictures, we found that Gatys et al [1] algorithm cannot perfectly reconstruct texture style. Output stylized picture could emerge unsatisfied unexpected textures such like muddiness in local area and insufficient grain expression. Our method bases on original algorithm, adding the Gradient Gram description on style loss, aiming to strengthen texture expression and eliminate muddiness. To some extent our method lengthens the runtime, however, its output stylized pictures get higher performance on texture details, especially in the elimination of muddiness.
- Subjects :
- 021110 strategic, defence & security studies
0209 industrial biotechnology
Stylized fact
business.industry
0211 other engineering and technologies
Pattern recognition
02 engineering and technology
Texture (geology)
Expression (mathematics)
Style (sociolinguistics)
Transfer (group theory)
020901 industrial engineering & automation
Artificial intelligence
business
[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Mathematics
Gram
Gramian matrix
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- 2020 39th Chinese Control Conference (CCC), 2020 39th Chinese Control Conference (CCC), Jul 2020, Shenyang, China. pp.7026-7030, ⟨10.23919/CCC50068.2020.9188652⟩
- Accession number :
- edsair.doi.dedup.....58368d9e5ff053dbcae722aa61a3be52
- Full Text :
- https://doi.org/10.23919/CCC50068.2020.9188652⟩