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GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation
- 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 Deep learning visualization tools presented at VIS Most tools are designed for experts
- 3. Many non-experts want to learn ML 3 Chris Olah’s Blog Andrej Karpathy’s Demo
- 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 TensorFlow Playground TensorFlow Playground was a great success http://playground.tensorflow.org
- 6. Modern deep models are very complex 5
- 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. 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. 7 How to explain this concept using visualization? Discriminator spots fake Police spots fake bills Generator synthesizes outputs Counterfeiter makes fake bills
- 10. GAN Lab First Interactive Tool for Learning GANs in Browser
- 11. Key contributions of GAN Lab • Novel visualization of GAN’s training process • Interactive model training • Browser-based implementation 8
- 12. How did we visualize GANs? 9 Discriminator spots fake Police spots fake bills Generator synthesizes outputs Counterfeiter makes fake bills
- 13. 2D data distribution, instead of high-dimensional images 10 What type of data to visualize? Discriminator (Police) Generator (Counterfeiter)
- 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. VER. 0.1 12 Real (green) Generated (purple)
- 16. How to visually explain the generator? 13 Generator (Counterfeiter)
- 17. How to visually explain the generator? map an input point into a new position random 14 Generator (Counterfeiter)
- 18. How to visually explain the generator? map an input point into a new position random Manifold ? 14 Generator (Counterfeiter)
- 19. Mixture of two Gaussians 15 How to visually explain the generator?
- 20. How to visualize the discriminator? 16 Generator (Counterfeiter) Discriminator (Police)
- 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. VER. 0.5 18
- 23. MODEL OVERVIEW GRAPH
- 24. 1. Building mental models for GANs How does it help?
- 25. 1. Building mental models for GANs 2. Tracking data flow How does it help?
- 26. 1. Building mental models for GANs 2. Tracking data flow 3. Locating hyperparameters How does it help?
- 27. GAN Lab broadens education access 24 Conventional Deep Learning Visualization in JavaScript in Python with GPU Model Training Visualization $$$
- 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. GAN Lab is Live! 20K visitors, 100+ countries 1.9K Likes 800+ Retweets Try at bit.ly/gan-lab
- 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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