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聊聊Fast neural style

這是在Tainan.py X MOUST中分享有關於Fast Neural Style的投影片

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聊聊Fast neural style

  1. 1. Fast Neural Style Sam Lee 2017.5.20 Tainan.py X MOUST
  2. 2. GAN 開始之前需要知道的東東 先講一下很潮的 Deep Learning
  3. 3. Fully-Connected Network
  4. 4. http://deeplearning.stanford.edu/wiki/index.php/Feature_extraction_using_convolutio Convolution and Pooling
  5. 5. Convolutional Neural Network http://www.cc.gatech.edu/~hays/compvision/proj6/
  6. 6. VGG https://www.cs.toronto.edu/~frossard/post/vgg16/
  7. 7. Neural Style Transfer 聽說電腦不只會揀土豆,還會作畫 ?
  8. 8. Images from: https://github.com/lengstrom/fast-style-transfer Styler Transfer
  9. 9. A Neural Algorithm of Artistic Style https://arxiv.org/abs/1508.06576 Leon A. Gatys, Alexander S. Ecker, Matthias Bethge Sep 2015
  10. 10. by Mark Chang Styler Transfer
  11. 11. Styler Transfer by Mark Chang
  12. 12. Image from: A Neural Algorithm of Artistic Style (Gatys et.) Layers
  13. 13. + Demo To see gif https://github.com/misgod/fast-neural-style-keras/blob/master/slide/demo.gif
  14. 14. 這這 ...... 看起來一點都不智慧看起來一點都不智慧 而且速度很慢吧而且速度很慢吧
  15. 15. GAN Generative Adversarial Networks 人工智慧也能「無中生有」嗎 ?
  16. 16. ICLR-2017 提交論文: 45 篇產生式模型相關, 37 篇與對抗訓練相關; NIPS-2016 :在會議大綱中 GAN 被提及超過 120 次; 同時,會議專門針對“ Adversarial Training” 組織了一個 workshop ,收錄 了 32 篇文章,絕大多數與 GAN 直接相關;此外,正會還收錄了 17 篇產生式 模型相關文章, 11 篇對抗訓練相關文章; Arxiv :在 Computer Science 分類下約有 500 篇與對抗網絡相關文章,其 中絕大多數為 2016 年的工作; 資料來源 : GAN模型及其在2016年度的进展 火紅的 GAN
  17. 17. Generative Adversarial Networks Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, et. Jun 2014 https://arxiv.org/abs/1406.2661
  18. 18. 圖片來源 GAN
  19. 19. GAN
  20. 20. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Alec Radford, Luke Metz, Soumith Chintala Jan 2016 https://arxiv.org/abs/1406.2661
  21. 21. DCGAN
  22. 22. DCGAN
  23. 23. DCGAN
  24. 24. DCGAN
  25. 25. https://deephunt.in/the-gan-zoo-79597dc8c347 The GAN Zoo • GAN • DCGAN • InfoGAN • WGAN • WGAN-GP • ...
  26. 26. GAN Fast Neural Style GAN+Style Transfer = Fast Neural Style
  27. 27. Perceptual Losses for Real-Time Style Transfer and Super-Resolution Johnson, Justin and Alahi, Alexandre and Fei-Fei, Li Mar 2016 https://arxiv.org/abs/1603.08155
  28. 28. Model
  29. 29. Image Transform Net
  30. 30. Instance Normalization: The Missing Ingredient for Fast Stylization Dmitry Ulyanov, Andrea Vedaldi, Victor Lempitsky Sep 2016 https://arxiv.org/abs/1607.08022
  31. 31. Instance Normalization Batch Normalization Instance Normalization
  32. 32. Instance Normalization
  33. 33. 圖片來源 :https://github.com/titu1994/Fast-Neural-Style 實作坑 - Border Artifact • Remove zero-padding • Add ReflectionPadding in Input, so input & output have same size
  34. 34. http://distill.pub/2016/deconv-checkerboard/ 實作坑 - Checkerboard Artifacts • switching deconvolutional layers for resize- convolution layers
  35. 35. GAN Demo 來看看成果吧 ... 程式碼 https://github.com/misgod/fast-neural-style-keras
  36. 36. Demo
  37. 37. Demo
  38. 38. Demo
  39. 39. Demo
  40. 40. Demo
  41. 41. Demo
  42. 42. Demo

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