"Generative Models"의 두 판 사이의 차이
ph
잔글 (→GAN) |
잔글 (→GAN) |
||
| 8번째 줄: | 8번째 줄: | ||
** [https://arxiv.org/abs/1606.00709 f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization] Sebastian Nowozin, Botond Cseke, Ryota Tomioka. Jun 2016 | ** [https://arxiv.org/abs/1606.00709 f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization] Sebastian Nowozin, Botond Cseke, Ryota Tomioka. Jun 2016 | ||
* https://blogs.nvidia.com/blog/2017/05/17/generative-adversarial-network | * https://blogs.nvidia.com/blog/2017/05/17/generative-adversarial-network | ||
| − | * [[ | + | * [[SimGAN]] 2017 CVPR best paper. by Apple. |
=VAE= | =VAE= | ||
2017년 7월 27일 (목) 11:29 기준 최신판
GAN
- http://kvfrans.com/generative-adversial-networks-explained/
- How to Train a GAN? Tips and tricks to make GANs work
- Generative Adversarial Networks (GANs) in 50 lines of code (PyTorch)
- real/fake 두 데이터 셋이 있어야 한다. D를 먼저 훈련시키고 다음에 G를 훈련시킨다.
- least squres GAN
- Tagger: Deep Unsupervised Perceptual Grouping
- f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization Sebastian Nowozin, Botond Cseke, Ryota Tomioka. Jun 2016
- https://blogs.nvidia.com/blog/2017/05/17/generative-adversarial-network
- SimGAN 2017 CVPR best paper. by Apple.
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
etc
GAN, VAE, pixel-rnn (by OpenAI)
https://blog.openai.com/generative-models/
GAN vs VAE
https://www.reddit.com/r/MachineLearning/comments/4r3pjy/variational_autoencoders_vae_vs_generative/