"Generative Models"의 두 판 사이의 차이
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** autoencoder와 동일하나 latent vector를 생성할 때 (unit) gaussian으로만 생성하도록 constraint를 줌. 그래서 unit gaussian random variable로부터 generate. | ** 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. | ** 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. | ||
* GAN, VAE, pixel-rnn (by OpenAI) : https://blog.openai.com/generative-models/ | * GAN, VAE, pixel-rnn (by OpenAI) : https://blog.openai.com/generative-models/ | ||
2017년 5월 4일 (목) 02:38 판
- GAN : http://kvfrans.com/generative-adversial-networks-explained/
- 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.
- GAN, VAE, pixel-rnn (by OpenAI) : https://blog.openai.com/generative-models/