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GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation

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Presentation at IEEE VIS 2018

Minsuk Kahng, Nikhil Thorat, Duen Horng (Polo) Chau, Fernanda Viégas, Martin Wattenberg.
GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation.
IEEE Transactions on Visualization and Computer Graphics, 25(1) (VAST 2018).

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GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation

  1. 1. GAN Lab Understanding Complex Deep Generative Models using Interactive Visual Experimentation Georgia Tech Google Minsuk Kahng Nikhil Thorat Polo Chau Fernanda Viégas Martin Wattenberg Georgia Tech Google Google PAIR | People + AI Research Initiative
  2. 2. 2 Deep learning visualization tools presented at VIS Most tools are designed for experts
  3. 3. Many non-experts want to learn ML 3 Chris Olah’s Blog Andrej Karpathy’s Demo
  4. 4. Many non-experts want to learn ML 3 Chris Olah’s Blog Andrej Karpathy’s Demo Can our VIS community help this population?
  5. 5. 5 TensorFlow Playground TensorFlow Playground was a great success http://playground.tensorflow.org
  6. 6. Modern deep models are very complex 5
  7. 7. Generative Adversarial Networks (GANs) 6 Hard to understand and train even for experts “the most interesting idea in the last 10 years in ML” - Yann LeCun Face images generated by BEGAN [Berthelot et al., 2017]
  8. 8. Why are GANs hard to understand? Because a GAN uses two competing neural networks 7 Discriminator spots fake Police spots fake bills Generator synthesizes outputs Counterfeiter makes fake bills
  9. 9. 7 How to explain this concept using visualization? Discriminator spots fake Police spots fake bills Generator synthesizes outputs Counterfeiter makes fake bills
  10. 10. GAN Lab First Interactive Tool for Learning GANs in Browser
  11. 11. Key contributions of GAN Lab • Novel visualization of GAN’s training process • Interactive model training • Browser-based implementation 8
  12. 12. How did we visualize GANs? 9 Discriminator spots fake Police spots fake bills Generator synthesizes outputs Counterfeiter makes fake bills
  13. 13. 2D data distribution, instead of high-dimensional images 10 What type of data to visualize? Discriminator (Police) Generator (Counterfeiter)
  14. 14. 2D data distribution, instead of high-dimensional images 1411 What type of data to visualize? Discriminator (Police) Generator (Counterfeiter) 1. Easier to visualize data distribution 2. Easier for learners to track dynamics Why 2D data points?
  15. 15. VER. 0.1 12 Real (green) Generated (purple)
  16. 16. How to visually explain the generator? 13 Generator (Counterfeiter)
  17. 17. How to visually explain the generator? map an input point into a new position random 14 Generator (Counterfeiter)
  18. 18. How to visually explain the generator? map an input point into a new position random Manifold ? 14 Generator (Counterfeiter)
  19. 19. Mixture of two Gaussians 15 How to visually explain the generator?
  20. 20. How to visualize the discriminator? 16 Generator (Counterfeiter) Discriminator (Police)
  21. 21. 2D heatmap, to represent its binary classification 17 How to visualize the discriminator? Samples in this region are likely real. Samples are likely fake.
  22. 22. VER. 0.5 18
  23. 23. MODEL OVERVIEW GRAPH
  24. 24. 1. Building mental models for GANs How does it help?
  25. 25. 1. Building mental models for GANs 2. Tracking data flow How does it help?
  26. 26. 1. Building mental models for GANs 2. Tracking data flow 3. Locating hyperparameters How does it help?
  27. 27. GAN Lab broadens education access 24 Conventional Deep Learning Visualization in JavaScript in Python with GPU Model Training Visualization $$$
  28. 28. Everything done in browser, powered by TensorFlow.js GAN Lab broadens education access 25 Accelerated by WebGL in JavaScript Visualization also in JavaScript Model Training
  29. 29. GAN Lab is Live! 20K visitors, 100+ countries 1.9K Likes 800+ Retweets Try at bit.ly/gan-lab
  30. 30. Georgia Tech Google PAIR Minsuk Kahng Nikhil Thorat Polo Chau Fernanda Viégas Martin Wattenberg Georgia Tech Google PAIR Google PAIR minsuk.comLearning and Playing with GANs in your browser! PAIR People + AI Research| GAN Lab | Try at bit.ly/gan-lab

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