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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 저널정보
- 한국정보통신학회 INTERNATIONAL CONFERENCE ON FUTURE INFORMATION & COMMUNICATION ENGINEERING 2022 INTERNATIONAL CONFERENCE ON FUTURE INFORMATION & COMMUNICATION ENGINEERING Vo.13 No.1
- 발행연도
- 2022.1
- 수록면
- 227 - 231 (5page)
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초록· 키워드
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The quality of training data is one of the most critical factors under constructing artificial intelligence systems using convolutional neural networks (CNNs). It is well known that larger data leads better performance on the convolutional neural networks. However, training a CNN with massive data requires tremendous training time under the restricted computing power condition. In some researches, pruning overlapped data can help to reduce the training time while keeping similar accuracy.
In this paper, we conduct an experimental analysis of the subset selection scheme according to various datasets, models, and data augmentation. The influential subset selection conditions for the deep learning model and its performance are described. Therefore, we believe that the reported experiments and information can be helpful for training deep learning models efficiently.
In this paper, we conduct an experimental analysis of the subset selection scheme according to various datasets, models, and data augmentation. The influential subset selection conditions for the deep learning model and its performance are described. Therefore, we believe that the reported experiments and information can be helpful for training deep learning models efficiently.
목차
Abstract
I. INTRODUCTION
II. INFLUENTIAL FACTORS FOR SUBSET SELECTION METHODS
III. EXPERIMENTAL ANALYSIS OF SUBSET SELECTION
IV. CONCLUSIONS
REFERENCES
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