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Soumith Chintala, Artificial Intelligence Research Engineer, Facebook at MLconf NYC - 4/15/16

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Predicting the Future Using Deep Adversarial Networks: Learning With No Labeled Data: Labeling data to solve a certain task can be expensive, slow and does not scale. If unsupervised learning works, then one can have very little labelled data to help a machine solve a particular task. Most traditional unsupervised learning methods such as PCA and K-means clustering do not work well for complicated data distributions, making them useless for a lot of tasks. In this talk, I’ll go over recent advances in a technique for unsupervised learning called Generative Adversarial networks, which can learn to generate very complicated data distributions such as images and videos. These trained adversarial networks are then used to solve new tasks with very little labeled data, making them an attractive class of algorithms for many domains where there is limited labeled data but unlimited unlabeled data.

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Soumith Chintala, Artificial Intelligence Research Engineer, Facebook at MLconf NYC - 4/15/16

  1. 1. Predicting the Future using Deep Adversarial Networks Soumith Chintala Facebook AI Research Learning With No Labeled Data
  2. 2. Overview of the talk • The problem at hand • What are the benefits? • How did we solve it • What have we achieved • What’s left?
  3. 3. You are here
  4. 4. You are here Walk here!
  5. 5. Route 1
  6. 6. Route 2
  7. 7. Route 3
  8. 8. Let’s Train a Route Generator
  9. 9. Route Generator Training Data
  10. 10. Linear Regressor with Mean-square Error Route Generator Route Generator
  11. 11. Linear Regressor with Mean-square Error Route Generator Route Generator noise
  12. 12. Linear Regressor with Mean-square Error Route Generator Route Generator noise Route
  13. 13. Linear Regressor with Mean-square Error Route Generator Route Generator noise Route Optimizer Training Data Loss: MSE
  14. 14. Route Generator Training Data
  15. 15. Linear Regressor with Mean-square Error Route Generator Eh?
  16. 16. Linear Regressor with Mean-square Error Route Generator Eh?
  17. 17. Linear Regressor with Mean-square Error Route Generator Eh? Eh?
  18. 18. Linear Regressor with Mean-square Error Route Generator Eh? Eh? Eh?
  19. 19. Linear Regressor with Mean-square Error Route Generator Eh? Eh? Eh? Eh?
  20. 20. Linear Regressor with Mean-square Error Route Generator Eh? Eh? Eh? Eh? Eh?
  21. 21. Linear Regressor with Mean-square Error Route Generator Problem!
 Converges to the mean of training samples
  22. 22. Linear Regressor with Mean-square Error Route Generator Which is not a valid route! Problem!
 Converges to the mean of training samples
  23. 23. Linear Regressor with Mean-square Error Route Generator Let’s try again!
  24. 24. Linear Regressor with Route Validator Route Generator Route Generator noise Route Optimizer Training Data Loss: MSEpoints
  25. 25. Route Generator Route Generator Route Optimizer Training Data Loss: Valid Route Linear Regressor with Route Validator noise points
  26. 26. Route Generator Route Generator Route Optimizer Training Data Loss: Valid Route Linear Regressor with Route Validator noise points
  27. 27. Route Generator Route Generator Route Optimizer Training Data Loss: Valid Route Linear Regressor with Route Validator ???? noise points
  28. 28. Generator Generator Sample Optimizer Training Data Loss: Looks Real???? noise points
  29. 29. Generator Generatornoise Sample Optimizer Training Data Loss: Looks Real
  30. 30. Generator Generatornoise Sample Classification Loss Training Data Learnt Real/Fake Cost function Discriminator
  31. 31. Generator Generatornoise Sample Classification Loss Training Data Neural Net Discriminator Neural Net
  32. 32. Generator Generatornoise Sample Classification Loss Training Data Discriminator Trained via Gradient Descent
  33. 33. Generator Generatornoise Sample Classification Loss Training Data Discriminator Optimizing to fool D
  34. 34. Generator Generatornoise Sample Classification Loss Training Data Discriminator Optimizing to not get fooled by G
  35. 35. Route 4
  36. 36. Route Crazy!
  37. 37. Route Crazy! But valid.
  38. 38. Generator Generator noise Sample Classification Loss Training Data Discriminator Optimizing to not get fooled by G class
  39. 39. Generator Generatornoise Sample Classification Loss Training Data Discriminator Optimizing to not get fooled by G
  40. 40. Generator Generatornoise Sample Classification Loss Training Data Discriminator Optimizing to not get fooled by G MSE Loss
  41. 41. Uses • Unsupervised Learning • Learn when there’s little labeled data • Planning • Look-ahead to take better decisions

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