"Generative Models"의 두 판 사이의 차이
ph
(→VAE) |
잔글 (→GAN) |
||
| (같은 사용자의 중간 판 7개는 보이지 않습니다) | |||
| 3번째 줄: | 3번째 줄: | ||
* [https://github.com/soumith/ganhacks How to Train a GAN? Tips and tricks to make GANs work] | * [https://github.com/soumith/ganhacks How to Train a GAN? Tips and tricks to make GANs work] | ||
* [https://medium.com/@devnag/generative-adversarial-networks-gans-in-50-lines-of-code-pytorch-e81b79659e3f Generative Adversarial Networks (GANs) in 50 lines of code (PyTorch)] | * [https://medium.com/@devnag/generative-adversarial-networks-gans-in-50-lines-of-code-pytorch-e81b79659e3f Generative Adversarial Networks (GANs) in 50 lines of code (PyTorch)] | ||
| − | ** real/fake 두 데이터 셋이 있어야 한다. D를 먼저 훈련시키고 다음에 G를 훈련시킨다. | + | ** real/fake 두 데이터 셋이 있어야 한다. D를 먼저 훈련시키고 다음에 G를 훈련시킨다. |
| + | * [http://wiseodd.github.io/techblog/2017/03/02/least-squares-gan/ least squres GAN] | ||
| + | * [http://papers.nips.cc/paper/6066-tagger-deep-unsupervised-perceptual-grouping Tagger: Deep Unsupervised Perceptual Grouping] | ||
| + | ** [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 | ||
| + | * [[SimGAN]] 2017 CVPR best paper. by Apple. | ||
=VAE= | =VAE= | ||
| 13번째 줄: | 18번째 줄: | ||
* [https://jmetzen.github.io/2015-11-27/vae.html VAE in tensorflow] | * [https://jmetzen.github.io/2015-11-27/vae.html VAE in tensorflow] | ||
| − | = GAN, VAE, pixel-rnn (by OpenAI)= | + | =etc= |
| + | ===GAN, VAE, pixel-rnn (by OpenAI)=== | ||
https://blog.openai.com/generative-models/ | https://blog.openai.com/generative-models/ | ||
| + | |||
| + | ===GAN vs VAE=== | ||
| + | https://www.reddit.com/r/MachineLearning/comments/4r3pjy/variational_autoencoders_vae_vs_generative/ | ||
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/