DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Shin, Jinwoo | - |
dc.contributor.advisor | 신진우 | - |
dc.contributor.author | Park, Jongjin | - |
dc.date.accessioned | 2021-05-11T19:33:50Z | - |
dc.date.available | 2021-05-11T19:33:50Z | - |
dc.date.issued | 2019 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=875364&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/283070 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 전기및전자공학부, 2019.8,[iii, 16 p. :] | - |
dc.description.abstract | Transfer learning methods are arguably the most trustworthy techniques for boosting the performance of machine learning models. However, for image generation tasks, the efficient methods for transferring the knowledge of pre-trained models have not been explored compared to image classification. In this paper, we propose a novel transfer method especially for generative adversarial networks. To transfer useful knowledge from the generator and discriminator from a teacher domain, we propose teacher mimic loss (i.e., how to effectively transfer the knowledge) and sampling method (i.e., how to select samples to transfer). We demonstrate the effectiveness of the proposed method empirically. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Deep learning▼atransfer learning▼agenerative adversarial networks▼aImportance sampling | - |
dc.subject | 딥러닝▼a전이학습▼a생성적 적대 신경망▼a중요도 샘플링 | - |
dc.title | Mimicking key samples for knowledge transfer of GAN | - |
dc.title.alternative | 키 샘플 모방을 통한 생성적 적대 신경망의 전이 학습 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :전기및전자공학부, | - |
dc.contributor.alternativeauthor | 박종진 | - |
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