[系列活動] 探索及應用生成對抗網路
•
31 likes•4,782 views
生成對抗網路 (Generative Adversarial Network, 簡稱 GAN) 是深度學習在 2017 年的最大焦點之一。 有大量的研究結果及讓人驚訝的應用。 單憑輪廓就能自動畫出生動的小貓、指定屬性就能生出成動漫角色、將圖片中的馬換成斑馬、將視訊中的直播主直接替換成另外一個人、從文字描述畫出圖片、 任意產生中文字型、改變照片中人的性別、膚色等等屬性。 可以說不了解 GAN的話，等於錯過了一大半 2017 的深度學習的進展。
Recommended
More Related Content
What's hot
What's hot (20)
More from 台灣資料科學年會
More from 台灣資料科學年會 (20)
[系列活動] 探索及應用生成對抗網路
- 4. 生成對抗網路簡介 魏澤人
- 5. 深度學習 = 現代煉金術? see also https://www.facebook.com/yann.lecun/posts/10154938130592143
- 10. 深度學習
- 12. Example
- 14. f( )=5 f( )=4 f( )=1
- 15. 生成模型 f(5) = f(4) = f(1) =
- 16. from Science Museum of Minnesota 結構 Structured Prediction
- 20. make.girls.moe
- 21. 例子: 字型
- 25. 現在的問題是 給定一些例子
- 27. 什麼叫做像？
- 30. ● Autoencoder ● Variational Autoencoder ● WaveNet ● Boltzmann ● Fully visible belief nets ● PixelRNN ● Generative Stochastic Network
- 31. ● 可以平行生成 ● Generator 的限制少 ● 不需要 Markov chains ( 高維度及計算量) ● 生出的結果較好 (但比較不好訓練) Why GAN?
- 33. Loss = cross entropy ● 可以看成零和 min-max 對奕 ● 理論上等於 minimize JS divergence
- 34. 實際上, G
- 35. By 李弘毅教授
- 36. By 李弘毅教授
- 37. Amazing GANs by Ironhead https://docs.google.com/presentation/d/14jJL6MR2uf4CD7PmsgMtivX8 WfK-QMdKdwrLEYH9qDA/edit#slide=id.g22c42edce6_0_644
- 38. 生成對抗網路的進展 魏澤人
- 39. 比喻 G 和 D 的宿命對決
- 41. 跟標準答案不一樣就打
- 43. 有什麼壞處？
- 44. Mode collapse
- 46. Metz et al. (2016)
- 47. Some solutions: ● Minibatch features ● Unrolled
- 48. 難訓練 ● https://github.com/soumith/ganhacks ● Towards Principled Methods for Training Generative Adversarial Networks
- 49. 先複習一點資訊理論
- 50. 熵 Entropy 用所需的資訊量來看 ● 4 種可能，各 ¼ 機率， 那需要 2bit 的資訊量來紀錄一次事件。 ● 8 種可能，各 ⅛ 機率， 那需要 3bit 的資訊量來紀錄一次事件。 ● 如果 a, b, 的機率各是 ¼ ， c,d,e,f 的機率各是 ⅛ , 那麼平均來說 2.5bit ● 所以 x 的機率是 p 時， I(x) = -log(p) ● H(X) 是平均資訊量 ● 或
- 51. Conditional Entropy 已知 Y, 是否有幫助我們知道 X? Joint Entropy X, Y 合起來有多少資訊量？
- 52. 所以 ● H(X, Y) ≤ H(X) + H(Y) ● H(X, Y) = H(Y) + H(X|Y) ● H(X, Y) = H(X) + H(Y|X) ● H(X|Y) = H(X,Y) - H(Y) ● Mutual information ○ I(X;Y) = H(X) + H(Y) - H(X, Y)
- 54. Cross Entropy ● 預測機率是 q ● 實際機率是 p 所需的資訊量
- 55. Kullback–Leibler divergence ● 以為機率分佈是 Q ● 實則機率分佈是 P 浪費了多少 bit ? = H(P, Q) - H(P)
- 58. Jensen–Shannon divergence Symmetrised divergence λ divergence
- 59. GAN? GAN loss 差不多就是 JS divergence
- 60. 參考： GAN loss =
- 62. 什麼叫做相似 （以機率分佈來說） ● KL ● JS ● Earth-Mover or Wassertein-1 ● Total Variation(TV)
- 63. A 有 32 種 δ=δ=
- 64. Wasserstein Distance 是所有的機率分佈 γ(x, y) ， 其 marginal 分別是
- 65. 0 ⅓ ⅓ ⅓ 0 ⅕ ⅕ ⅕ ⅕ ⅕
- 66. 0 ⅕ 2/15 ⅓ 1/15 ⅕ 1/15 ⅓ 2/15 ⅕ ⅓ 0 ⅕ ⅕ ⅕ ⅕ ⅕
- 68. 0 2/15 1/5 ⅓ 1/15 ⅕ 1/15 ⅓ 1/5 2/15 ⅓ 0 ⅕ ⅕ ⅕ ⅕ ⅕
- 73. Wasserstein Distance 是所有的機率分佈 γ(x, y) ， 其 marginal 分別是
- 75. 是 Compact Space， 如”盒子”
- 77. Least Squares Generative Adversarial Networks https://arxiv.org/abs/1611.04076
- 78. When a,b,c = -1, 1, 0, it minimize Pearson chi-square divergence
- 79. f-divergence
- 80. WGAN with Gradient Penalty Improved Training of Wasserstein GANs https://arxiv.org/abs/1704.00028
- 85. DRAGAN Deep Regret Analytic Generative Adversarial Networks On Convergence and Stability of GANs https://arxiv.org/abs/1705.07215
- 88. 跟標準答案不一樣就打？
- 89. ● WGAN ○ 不要打太多 ● WGAN gp ○ 打小力一點 ● DRAGAN ○ 分數高的時候，打小力一點
- 91. BEGAN Boundary Equilibrium Generative Adversarial Networks https://arxiv.org/abs/1703.10717
- 93. Progressive Growing of GANs for Improved Quality, Stability, and Variation http://research.nvidia.com/publication/2017-10_Progressive-Growing-of
- 98. Pix2Pix Image-to-Image Translation with Conditional Adversarial Networks Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros
- 100. G
- 101. 不就是監督式學習？
- 102. G D 真 假
- 103. Experiment
- 104. L1
- 105. MSE
- 106. Variation
- 107. MSE
- 108. L1
- 109. Pix2Pix
- 110. Pix2Pix
- 111. CycleGAN Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks Jun-Yan Zhu*, Taesung Park*, Phillip Isola, Alexei A. Efros Berkeley AI Research Lab, UC Berkeley
- 113. G(x) ≈Y, F(y) ≈ X F(G(x)) = x, G(F(y)) = y
- 127. 從論文到程式碼 魏澤人
- 128. Dataset? 可以先從 MNIST 和 Cifar10 開始
- 129. 先回到早期的論文
- 130. Generative Adversarial Networks (GAN) https://arxiv.org/abs/1406.2661
- 131. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks https://arxiv.org/abs/1511.06434 (DCGAN)
- 133. How to train a GAN https://github.com/soumith/ganhacks ● Normalize the inputs ● A modified loss function ● Use a spherical Z ● BatchNorm/Instance Normalization ● Avoid Sparse Gradients: ReLU, MaxPool ● Use Soft and Noisy Labels ● Use stability tricks from RL ● Use the ADAM Optimizer
- 134. G 和 D 的網路要怎麼實作？ 我的實作 https://github.com/tjwei/GANotebooks
- 135. Keras
- 136. 訓練 netD
- 137. 訓練 netG
- 138. Lasagne
- 139. Tensorflow
- 141. PyTorch
- 142. Use a spherical Z np.random.normal(size=(batchSize, nz)) 可以先看看 nz = 32~128
- 143. 簡單的 Generator
- 144. 簡單的 Discriminator
- 145. Batchnorm
- 147. 起始權重
- 148. WGAN 訓練的迴圈
- 149. Weight Clipping
- 150. Gradient Penalty
- 151. 可以加入的東西 ● ACGAN ○ https://arxiv.org/abs/1610.09585 ● InfoGAN ○ https://arxiv.org/abs/1606.03657 ● Conditional GAN ○ https://arxiv.org/abs/1411.1784 ● Semi-supervised GAN ○ https://arxiv.org/abs/1606.01583
- 152. make.girls.moe
- 157. Data?
- 158. LSUN
- 160. CelebA
- 163. make.girls.moe
- 167. ● facades: 400 images from the CMP Facades dataset. [Citation] ● cityscapes: 2975 images from the Cityscapes training set. [Citation] ● maps: 1096 training images scraped from Google Maps. ● horse2zebra: 939 horse images and 1177 zebra images downloaded from ImageNet using keywords wild horse and zebra ● apple2orange: 996 apple images and 1020 orange images downloaded from ImageNet using keywords apple and navel orange. ● summer2winter_yosemite: 1273 summer Yosemite images and 854 winter Yosemite images were downloaded using Flickr API. See more details in our paper. ● monet2photo, vangogh2photo, ukiyoe2photo, cezanne2photo: The art images were downloaded from Wikiart. The real photos are downloaded from Flickr using the combination of the tags landscape and landscapephotography. The training set size of each class is Monet:1074, Cezanne:584, Van Gogh:401, Ukiyo-e:1433, Photographs:6853. ● iphone2dslr_flower: both classes of images were downlaoded from Flickr. The training set size of each class is iPhone:1813, DSLR:3316. See more details in our paper.
- 179. 更多應用
- 180. Super Resolution
- 182. StackGAN (CUB test set)
- 183. StackGAN (Oxford-102 test set & COCO validation set)
- 184. TAC-GAN
- 185. iGAN
- 192. ● 聲音? ● 文字? ● Sequence? ● Reinforcement Learning? ○ Connecting Generative Adversarial Networks and Actor-Critic Methods ○ Automatic Goal Generation for Reinforcement Learning Agents