Generative Models - 편집 역사

Admin: /* GAN */

2017-07-27T02:29:40Z

GAN

2017-07-26T09:48:25Z

GAN

2017-05-26T06:21:25Z

GAN

2017-05-26T06:20:35Z

GAN

2017-05-09T15:17:13Z

etc

2017-05-09T15:16:31Z

GAN, VAE, pixel-rnn (by OpenAI)

2017-05-04T05:27:59Z

2017-05-04T05:27:40Z

GAN

2017-05-04T05:20:56Z

VAE

2017-05-04T05:18:21Z

@@ 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://blogs.nvidia.com/blog/2017/05/17/generative-adversarial-network
-* [[Learning from Simulated and Unsupervised Images through Adversarial Training|SimGAN]] 2017 CVPR best paper. by Apple.
+* [[SimGAN]] 2017 CVPR best paper. by Apple.
 =VAE=

@@ 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://blogs.nvidia.com/blog/2017/05/17/generative-adversarial-network
 =VAE=

@@ 7번째 줄: / 7번째 줄: @@
 * [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/?ncid=em-ded-ng-14662&mkt_tok=eyJpIjoiTVdZeE1tRTBaREpoTnprMSIsInQiOiIxWXpleUVRNlRlOTZjZkIyaHpcL3M5b2t4UEtRVGVySDlIMEREK091UUJLRCs4dEtcL3lhXC9la3Bzdkp3djBIK2xLMU81Y3MreU1ZQXJKbHlRUTdyaWNENUZJVmVkUXBQVnJWUlp2QzJWbCtlOVg5YzNjQXJPN3o5VUUxR3o3OVowVSJ9]
+* https://blogs.nvidia.com/blog/2017/05/17/generative-adversarial-network
 =VAE=

@@ 7번째 줄: / 7번째 줄: @@
 * [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
 =VAE=

@@ 17번째 줄: / 17번째 줄: @@
 =etc=
-==GAN, VAE, pixel-rnn (by OpenAI)==
+===GAN, VAE, pixel-rnn (by OpenAI)===
 https://blog.openai.com/generative-models/
-==GAN vs VAE==
+===GAN vs VAE===
 https://www.reddit.com/r/MachineLearning/comments/4r3pjy/variational_autoencoders_vae_vs_generative/

@@ 16번째 줄: / 16번째 줄: @@
 * [https://jmetzen.github.io/2015-11-27/vae.html VAE in tensorflow]
-= GAN, VAE, pixel-rnn (by OpenAI)=
+=etc=
 https://blog.openai.com/generative-models/

@@ 5번째 줄: / 5번째 줄: @@
 ** 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]
 =VAE=

@@ 3번째 줄: / 3번째 줄: @@
 * [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)]
 ** real/fake 두 데이터 셋이 있어야 한다. D를 먼저 훈련시키고 다음에 G를 훈련시킨다.
 =VAE=

@@ 7번째 줄: / 7번째 줄: @@
 =VAE=
 * http://kvfrans.com/variational-autoencoders-explained/
-* 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.
+** 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.
+** we can compare generated images directly to the originals, which is '''''not possible''''' when using a GAN.
 * [https://jmetzen.github.io/2015-11-27/vae.html VAE in tensorflow]
 = GAN, VAE, pixel-rnn (by OpenAI)=
 https://blog.openai.com/generative-models/