DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Kim, Munchurl | - |
dc.contributor.advisor | 김문철 | - |
dc.contributor.author | Kim, Sae Hun | - |
dc.date.accessioned | 2021-05-13T19:39:17Z | - |
dc.date.available | 2021-05-13T19:39:17Z | - |
dc.date.issued | 2020 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=925218&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/285054 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 전기및전자공학부, 2020.8,[iv, 39 p. :] | - |
dc.description.abstract | Since various objects in anime have their own unique styles, anime style transfer can be seen as an object-to-object multi-style transfer problem. However, the state-of-the-art generative adversarial networks (GAN) for anime style transfer fails to transfer each real-world object to the corresponding anime object style properly. This is because the unsupervised learning cannot provide the semantic mappings between the multi-style objects. In this paper, we propose a new learning framework, called pseudo-supervised learning with a new GAN model, called AnimeGAN. Pseudo-supervised learning utilizes pseudo ground truths for multi-style anime objects so that our AnimeGAN can stably learn the semantic mappings between the real-world and multi-style anime objects. Moreover, we propose a novel single generator network that can embrace the multiple styles of various anime objects. For this, our generator is specifically designed to have three effective processing blocks: densely-connected channel attention block (DCCAB), down-scaling channel attention block (DSCAB), and up-scaling channel attention block (USCAB). Qualitative and quantitative evaluations show that our AnimeGAN generates much more pleasing anime-styled images than the state-of-the-art models. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Image-to-Image Translation▼aStyle Transfer▼aGenerative Adversarial Networks | - |
dc.subject | 이미지 변환▼a스타일 변환▼a적대적 생성 신경망 | - |
dc.title | Semantic multi-style transfer using pseudo-supervised learning for anime style transfer | - |
dc.title.alternative | 가(假)지도 학습을 이용한 의미론적 다중 스타일 변환에 관한 연구 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :전기및전자공학부, | - |
dc.contributor.alternativeauthor | 김세훈 | - |
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