"Generative Models"의 두 판 사이의 차이
ph
| 1번째 줄: | 1번째 줄: | ||
=GAN= | =GAN= | ||
| − | http://kvfrans.com/generative-adversial-networks-explained/ | + | * http://kvfrans.com/generative-adversial-networks-explained/ |
| + | * [https://github.com/soumith/ganhacks How to Train a GAN? Tips and tricks to make GANs work] | ||
=VAE= | =VAE= | ||
2017년 5월 4일 (목) 02:47 판
GAN
- http://kvfrans.com/generative-adversial-networks-explained/
- How to Train a GAN? Tips and tricks to make GANs work
VAE
- http://kvfrans.com/variational-autoencoders-explained/
- autoencoder와 동일하나 latent vector를 생성할 때 (unit) gaussian으로만 생성하도록 constraint를 줌. 그래서 unit gaussian random variable로부터 generate.
- In practice, there's a tradeoff between how accurate our network can be and how close its latent variables can match the unit gaussian distribution.
- latent vector를 바로 만들지도 않고 mean, std만 만들어낸다.
- we can compare generated images directly to the originals, which is not possible when using a GAN.
- VAE in tensorflow