Matches in SemOpenAlex for { <https://semopenalex.org/work/W4320509432> ?p ?o ?g. }
Showing items 1 to 81 of
81
with 100 items per page.
- W4320509432 abstract "AI-Generated Content (AIGC) has recently gained a surge in popularity, powered by its high efficiency and consistency in production, and its capability of being customized and diversified. The cross-modality nature of the representation learning mechanism in most AIGC technology allows for more freedom and flexibility in exploring new types of art that would be impossible in the past. Inspired by the pictogram subset of Chinese characters, we proposed PaCaNet, a CycleGAN-based pipeline for producing novel artworks that fuse two different art types, traditional Chinese painting and calligraphy. In an effort to produce stable and diversified output, we adopted three main technical innovations: 1. Using one-shot learning to increase the creativity of pre-trained models and diversify the content of the fused images. 2. Controlling the preference over generated Chinese calligraphy by freezing randomly sampled parameters in pre-trained models. 3. Using a regularization method to encourage the models to produce images similar to Chinese paintings. Furthermore, we conducted a systematic study to explore the performance of PaCaNet in diversifying fused Chinese painting and calligraphy, which showed satisfying results. In conclusion, we provide a new direction of creating arts by fusing the visual information in paintings and the stroke features in Chinese calligraphy. Our approach creates a unique aesthetic experience rooted in the origination of Chinese hieroglyph characters. It is also a unique opportunity to delve deeper into traditional artwork and, in doing so, to create a meaningful impact on preserving and revitalizing traditional heritage." @default.
- W4320509432 created "2023-02-14" @default.
- W4320509432 creator A5033632697 @default.
- W4320509432 creator A5038561808 @default.
- W4320509432 creator A5045830368 @default.
- W4320509432 creator A5057468947 @default.
- W4320509432 creator A5079731588 @default.
- W4320509432 creator A5088457247 @default.
- W4320509432 creator A5090989509 @default.
- W4320509432 creator A5091219117 @default.
- W4320509432 date "2023-01-30" @default.
- W4320509432 modified "2023-09-29" @default.
- W4320509432 title "PaCaNet: A Study on CycleGAN with Transfer Learning for Diversifying Fused Chinese Painting and Calligraphy" @default.
- W4320509432 doi "https://doi.org/10.48550/arxiv.2301.13082" @default.
- W4320509432 hasPublicationYear "2023" @default.
- W4320509432 type Work @default.
- W4320509432 citedByCount "0" @default.
- W4320509432 crossrefType "posted-content" @default.
- W4320509432 hasAuthorship W4320509432A5033632697 @default.
- W4320509432 hasAuthorship W4320509432A5038561808 @default.
- W4320509432 hasAuthorship W4320509432A5045830368 @default.
- W4320509432 hasAuthorship W4320509432A5057468947 @default.
- W4320509432 hasAuthorship W4320509432A5079731588 @default.
- W4320509432 hasAuthorship W4320509432A5088457247 @default.
- W4320509432 hasAuthorship W4320509432A5090989509 @default.
- W4320509432 hasAuthorship W4320509432A5091219117 @default.
- W4320509432 hasBestOaLocation W43205094321 @default.
- W4320509432 hasConcept C105795698 @default.
- W4320509432 hasConcept C130160918 @default.
- W4320509432 hasConcept C138885662 @default.
- W4320509432 hasConcept C142362112 @default.
- W4320509432 hasConcept C153349607 @default.
- W4320509432 hasConcept C154945302 @default.
- W4320509432 hasConcept C166957645 @default.
- W4320509432 hasConcept C191935318 @default.
- W4320509432 hasConcept C199360897 @default.
- W4320509432 hasConcept C205783811 @default.
- W4320509432 hasConcept C2780598303 @default.
- W4320509432 hasConcept C2781051154 @default.
- W4320509432 hasConcept C33923547 @default.
- W4320509432 hasConcept C41008148 @default.
- W4320509432 hasConcept C41895202 @default.
- W4320509432 hasConcept C43521106 @default.
- W4320509432 hasConcept C526940114 @default.
- W4320509432 hasConcept C7220189 @default.
- W4320509432 hasConcept C95457728 @default.
- W4320509432 hasConceptScore W4320509432C105795698 @default.
- W4320509432 hasConceptScore W4320509432C130160918 @default.
- W4320509432 hasConceptScore W4320509432C138885662 @default.
- W4320509432 hasConceptScore W4320509432C142362112 @default.
- W4320509432 hasConceptScore W4320509432C153349607 @default.
- W4320509432 hasConceptScore W4320509432C154945302 @default.
- W4320509432 hasConceptScore W4320509432C166957645 @default.
- W4320509432 hasConceptScore W4320509432C191935318 @default.
- W4320509432 hasConceptScore W4320509432C199360897 @default.
- W4320509432 hasConceptScore W4320509432C205783811 @default.
- W4320509432 hasConceptScore W4320509432C2780598303 @default.
- W4320509432 hasConceptScore W4320509432C2781051154 @default.
- W4320509432 hasConceptScore W4320509432C33923547 @default.
- W4320509432 hasConceptScore W4320509432C41008148 @default.
- W4320509432 hasConceptScore W4320509432C41895202 @default.
- W4320509432 hasConceptScore W4320509432C43521106 @default.
- W4320509432 hasConceptScore W4320509432C526940114 @default.
- W4320509432 hasConceptScore W4320509432C7220189 @default.
- W4320509432 hasConceptScore W4320509432C95457728 @default.
- W4320509432 hasLocation W43205094321 @default.
- W4320509432 hasOpenAccess W4320509432 @default.
- W4320509432 hasPrimaryLocation W43205094321 @default.
- W4320509432 hasRelatedWork W192911520 @default.
- W4320509432 hasRelatedWork W2348270136 @default.
- W4320509432 hasRelatedWork W2349026792 @default.
- W4320509432 hasRelatedWork W2380755093 @default.
- W4320509432 hasRelatedWork W2383359447 @default.
- W4320509432 hasRelatedWork W2389430004 @default.
- W4320509432 hasRelatedWork W2390113881 @default.
- W4320509432 hasRelatedWork W2934616944 @default.
- W4320509432 hasRelatedWork W4320509432 @default.
- W4320509432 hasRelatedWork W2090597924 @default.
- W4320509432 isParatext "false" @default.
- W4320509432 isRetracted "false" @default.
- W4320509432 workType "article" @default.