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Information-based Boundary Equilibrium Generative Adversarial Networks with Disentangled Representation Learning : 데이터의 특징을 해석 가능하게 학습하여 양질의 다양한 이미지를 생성하는 개선된 알고리즘에 관한 연구
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- Authors
- Advisor
- 이재욱
- Major
- 공과대학 산업공학과
- Issue Date
- 2018-02
- Publisher
- 서울대학교 대학원
- Keywords
- Generative Adversarial Networks ; Disentangled representation learning ; Mutual information
- Description
- 학위논문 (석사)-- 서울대학교 대학원 : 공과대학 산업공학과, 2018. 2. 이재욱.
- Abstract
- This paper describes a new image generation algorithm, developed from BEGAN. With an information-theoretic extension to BEGAN, this new algorithm is able to learn disentangled representations from image data sets. The information-theoretic extension was inspired by InfoGAN, a generative adversarial network that maximizes the mutual information between small subset of the latent variables and the observation. By using the proposed method of this paper, one can manipulate the generated images as desired by controlling the latent codes of input variables. Also the visual qualities of produced images are highly maintained.
- Language
- English
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