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A STUDY ON STACKED AUTOENCODERS AND ITS FINE-TUNING : 적층 자가인코더의 지도학습적 활용에 대한 연구
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- Authors
- Advisor
- 박병욱
- Major
- 자연과학대학 통계학과
- Issue Date
- 2015-02
- Publisher
- 서울대학교 대학원
- Keywords
- Stacked autoencoder
- Description
- 학위논문 (석사)-- 서울대학교 대학원 : 통계학과, 2015. 2. 박병욱.
- Abstract
- A stacked autoencoder is a kind of unsupervised deep learning algorithm which looks like the automatically learning features of input data, such as edges and objects in images. Stacked autoencoders have been used as building blocks to build and initialize multi-layer neural networks. Neural networks based on them have shown outstanding performance in natural images and speeches classification tasks. In this paper, we especially focus on the image analysis. We first introduce neural networks and autoencoders, and provide an explanation of what autoencoders actually learn. Then, we explain how to stack up autoencoders and how to use the fine-tuning method for the purpose of making a high-performance image classifier. Finally, we carry out a numerical study with MNIST handwritten digit database.
- Language
- English
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