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Neural Style Transfer for Picture with Gradient Gram Matrix Description

Authors :
Heng Jin
Tian Wang
Mengyi Zhang
Hichem Snoussi
Mingmin Li
Yan Wang
Shaanxi University of Science and Technology
University of California [San Francisco] (UCSF)
University of California
Laboratoire Modélisation et Sûreté des Systèmes (LM2S)
Institut Charles Delaunay (ICD)
Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS)-Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS)
University of California [San Francisco] (UC San Francisco)
University of California (UC)
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.

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⟩