-
# Data Augmentation in Emotion Classification Using Generative Adversarial Networks #
- Author: Xinyue Zhu, Yifan Liu, Zengchang Qin, Jiahong Li
- Origin: https://arxiv.org/abs/1711.00648
- Relat…
-
https://arxiv.org/abs/1511.06434
https://hyeongminlee.github.io/post/gan003_dcgan/
https://github.com/HyeongminLEE/Tensorflow_DCGAN
http://jaejunyoo.blogspot.com/2017/02/deep-convolutional-gan-dcga…
-
https://arxiv.org/abs/1710.10916
-
https://arxiv.org/abs/1812.04948
-
# Abstract
Generative Adversarial Networks(GAN)은 데이터 생성에서 뛰어난 모습을 보이고 있다. 많은 영역에서 쓰이고 있지만 여전히 안정적인 학습에는 어려움이 따른다. 문제점으로는 Nash-equilibrium, internal covariate shift, mode collapse, vanishing gradient,…
-
**Is your feature request related to a problem? Please describe.**
A clear and concise description of what the problem is.
Yes, Image Style Transfer addresses a specific type of problem in the fiel…
-
https://arxiv.org/abs/1703.06490v1
> Access to electronic health records (EHR) data has motivated computational advances in medical research. However, various concerns, particularly over privacy, c…
-
ACM Multimedia, (2019).
Attention付きConditional GAN(ACGAN)を提案し,Video Summarizationにおいて,SumMeとTVSumでSOTAを達成.
: human-AI cooperative music …
-
## 📝 Introduction
* 논문 분야: Computer Vision
* 논문에서 정의한 문제: GAN 학습의 불안정성, CNN 을 활용한 unsupervised learning 연구의 부족
* 논문에서 제안한 방법론: CNN 을 활용하여 GAN 학습 안정화
- all convolutional net
- eliminatin…