DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 정혜원 | - |
dc.contributor.author | Cha, Seunghun | - |
dc.contributor.author | 차승훈 | - |
dc.date.accessioned | 2024-07-30T19:31:32Z | - |
dc.date.available | 2024-07-30T19:31:32Z | - |
dc.date.issued | 2024 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1097185&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/321613 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 전기및전자공학부, 2024.2,[iii, 21 p. :] | - |
dc.description.abstract | In recent advancements in deep learning, there is a continuous generation of large datasets, and mitigating the costs associated with handling these datasets is a significant concern. Dataset condensation is a research area generating small size of synthetic dataset preserving information of original datasets. Among the various approaches to creating synthetic data, we discuss issues with methodologies that incorporate information from original datasets during the network training process. As an enhancement strategy, we propose an algorithm that generates synthetic data reflecting the training dynamics of the network and evaluate its performance. We showed that a methodology reflecting the training dynamics has the potential to generate synthetic data that more accurately captures the information of original datasets. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | 데이터셋 압축▼a합성 데이터▼a훈련 역학▼a데이터 효율적 학습▼a최적화 | - |
dc.subject | Dataset condensation▼aSynthetic dataset▼aTraining dynamics▼aData efficient learning▼aOptimization | - |
dc.title | Dataset condensation reflecting the training dynamics | - |
dc.title.alternative | 훈련 역학을 반영한 데이터셋 압축 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :전기및전자공학부, | - |
dc.contributor.alternativeauthor | Chung, Hye Won | - |
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