Matches in SemOpenAlex for { <https://semopenalex.org/work/W4383890717> ?p ?o ?g. }
Showing items 1 to 89 of
89
with 100 items per page.
- W4383890717 endingPage "337" @default.
- W4383890717 startingPage "326" @default.
- W4383890717 abstract "Recently, deep learning technology has made a breakthrough in computer vision, image processing, and other fields. Some researchers suggested neural style transfer method using a convolutional neural network (CNN). They established the correlation of features in a neural network to be treated as the style. However, their performance is unacceptable for Chinese landscape painting. According to the property of the Chinese landscape painting, this paper proposes a novel two stage style transfer method that imitates multiple styles of Chinese landscape painting based on deep learning. The structure of an input photo was simplified in the first stage. Then, the result of the first stage was transferred into the final stylized image in second stage. A generative adversarial network (GAN) is applied to train in each stage. Besides, a novel loss function was proposed to keep the shape of the content image. Finally, our method haves successfully imitated several styles of Chinese Landscape ink painting." @default.
- W4383890717 created "2023-07-12" @default.
- W4383890717 creator A5016053771 @default.
- W4383890717 creator A5042392794 @default.
- W4383890717 creator A5069212526 @default.
- W4383890717 creator A5087038763 @default.
- W4383890717 date "2023-01-01" @default.
- W4383890717 modified "2023-09-27" @default.
- W4383890717 title "A Structure-Aware Deep Learning Network for the Transfer of Chinese Landscape Painting Style" @default.
- W4383890717 cites W2112796928 @default.
- W4383890717 cites W2158240273 @default.
- W4383890717 cites W2194775991 @default.
- W4383890717 cites W2331128040 @default.
- W4383890717 cites W2475287302 @default.
- W4383890717 cites W2572730214 @default.
- W4383890717 cites W2603777577 @default.
- W4383890717 cites W2604721644 @default.
- W4383890717 cites W2604737827 @default.
- W4383890717 cites W2740729727 @default.
- W4383890717 cites W2962818016 @default.
- W4383890717 cites W2963073614 @default.
- W4383890717 cites W2963800363 @default.
- W4383890717 cites W2963890275 @default.
- W4383890717 cites W845365781 @default.
- W4383890717 doi "https://doi.org/10.1007/978-3-031-34732-0_25" @default.
- W4383890717 hasPublicationYear "2023" @default.
- W4383890717 type Work @default.
- W4383890717 citedByCount "0" @default.
- W4383890717 crossrefType "book-chapter" @default.
- W4383890717 hasAuthorship W4383890717A5016053771 @default.
- W4383890717 hasAuthorship W4383890717A5042392794 @default.
- W4383890717 hasAuthorship W4383890717A5069212526 @default.
- W4383890717 hasAuthorship W4383890717A5087038763 @default.
- W4383890717 hasConcept C108583219 @default.
- W4383890717 hasConcept C115961682 @default.
- W4383890717 hasConcept C127313418 @default.
- W4383890717 hasConcept C139719470 @default.
- W4383890717 hasConcept C142362112 @default.
- W4383890717 hasConcept C146357865 @default.
- W4383890717 hasConcept C151730666 @default.
- W4383890717 hasConcept C153349607 @default.
- W4383890717 hasConcept C154945302 @default.
- W4383890717 hasConcept C162324750 @default.
- W4383890717 hasConcept C205783811 @default.
- W4383890717 hasConcept C2776445246 @default.
- W4383890717 hasConcept C2993157417 @default.
- W4383890717 hasConcept C31972630 @default.
- W4383890717 hasConcept C38935604 @default.
- W4383890717 hasConcept C39890363 @default.
- W4383890717 hasConcept C41008148 @default.
- W4383890717 hasConcept C50644808 @default.
- W4383890717 hasConcept C81363708 @default.
- W4383890717 hasConceptScore W4383890717C108583219 @default.
- W4383890717 hasConceptScore W4383890717C115961682 @default.
- W4383890717 hasConceptScore W4383890717C127313418 @default.
- W4383890717 hasConceptScore W4383890717C139719470 @default.
- W4383890717 hasConceptScore W4383890717C142362112 @default.
- W4383890717 hasConceptScore W4383890717C146357865 @default.
- W4383890717 hasConceptScore W4383890717C151730666 @default.
- W4383890717 hasConceptScore W4383890717C153349607 @default.
- W4383890717 hasConceptScore W4383890717C154945302 @default.
- W4383890717 hasConceptScore W4383890717C162324750 @default.
- W4383890717 hasConceptScore W4383890717C205783811 @default.
- W4383890717 hasConceptScore W4383890717C2776445246 @default.
- W4383890717 hasConceptScore W4383890717C2993157417 @default.
- W4383890717 hasConceptScore W4383890717C31972630 @default.
- W4383890717 hasConceptScore W4383890717C38935604 @default.
- W4383890717 hasConceptScore W4383890717C39890363 @default.
- W4383890717 hasConceptScore W4383890717C41008148 @default.
- W4383890717 hasConceptScore W4383890717C50644808 @default.
- W4383890717 hasConceptScore W4383890717C81363708 @default.
- W4383890717 hasLocation W43838907171 @default.
- W4383890717 hasOpenAccess W4383890717 @default.
- W4383890717 hasPrimaryLocation W43838907171 @default.
- W4383890717 hasRelatedWork W2731899572 @default.
- W4383890717 hasRelatedWork W2964137521 @default.
- W4383890717 hasRelatedWork W2999805992 @default.
- W4383890717 hasRelatedWork W3011074480 @default.
- W4383890717 hasRelatedWork W3116150086 @default.
- W4383890717 hasRelatedWork W3133861977 @default.
- W4383890717 hasRelatedWork W4200173597 @default.
- W4383890717 hasRelatedWork W4291897433 @default.
- W4383890717 hasRelatedWork W4312417841 @default.
- W4383890717 hasRelatedWork W4321369474 @default.
- W4383890717 isParatext "false" @default.
- W4383890717 isRetracted "false" @default.
- W4383890717 workType "book-chapter" @default.