Graph generative adversarial network
In this report, we briefly explain the building blocks of Generative Adversarial Network (GAN), recent research on generalization of Convolution Neural Network (CNN) to graphs, and experimented on further usage of graph convolution on other types of model. We also proposed a simple method to upsampl...
Saved in:
主要作者: | |
---|---|
其他作者: | |
格式: | Final Year Project |
語言: | English |
出版: |
2018
|
主題: | |
在線閱讀: | http://hdl.handle.net/10356/73993 |
標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
總結: | In this report, we briefly explain the building blocks of Generative Adversarial Network (GAN), recent research on generalization of Convolution Neural Network (CNN) to graphs, and experimented on further usage of graph convolution on other types of model. We also proposed a simple method to upsample graphs. Experiments include usage of graph convolution on Variational Autoencoder (VAE) and GAN. Comparison of result between traditional and proposed graph VAE are observed showing graph VAE achieving better performance. An experimentation on Graph GAN shows the model is unable to converge. Problem analysis and idea for improvement on graph convolution and upsampling are given. |
---|