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.

1. Predicting the Future using
Deep Adversarial Networks
Soumith Chintala
Facebook AI Research
Learning With No Labeled Data

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. You are here

4. You are here
Walk here!

5. Route 1

6. Route 2

7. Route 3

8. Let’s Train a Route Generator

9. Route Generator
Training
Data

10. Linear Regressor with
Mean-square Error
Route Generator
Route
Generator

11. Linear Regressor with
Mean-square Error
Route Generator
Route
Generator
noise

12. Linear Regressor with
Mean-square Error
Route Generator
Route
Generator
noise Route

13. Linear Regressor with
Mean-square Error
Route Generator
Route
Generator
noise Route Optimizer
Training
Data
Loss: MSE

14. Route Generator
Training
Data

15. Linear Regressor with
Mean-square Error
Route Generator
Eh?

16. Linear Regressor with
Mean-square Error
Route Generator
Eh?

17. Linear Regressor with
Mean-square Error
Route Generator
Eh? Eh?

18. Linear Regressor with
Mean-square Error
Route Generator
Eh? Eh? Eh?

19. Linear Regressor with
Mean-square Error
Route Generator
Eh? Eh? Eh? Eh?

20. Linear Regressor with
Mean-square Error
Route Generator
Eh? Eh? Eh? Eh? Eh?

21. Linear Regressor with
Mean-square Error
Route Generator
Problem!
Converges to the mean
of training samples

22. Linear Regressor with
Mean-square Error
Route Generator
Which is not a valid route!
Problem!
Converges to the mean
of training samples

23. Linear Regressor with
Mean-square Error
Route Generator
Let’s try again!

24. Linear Regressor with
Route Validator
Route Generator
Route
Generator
noise
Route Optimizer
Training
Data
Loss: MSEpoints

25. Route Generator
Route
Generator
Route Optimizer
Training
Data
Loss:
Valid Route
Linear Regressor with
Route Validator
noise
points

26. Route Generator
Route
Generator
Route Optimizer
Training
Data
Loss:
Valid Route
Linear Regressor with
Route Validator
noise
points

27. Route Generator
Route
Generator
Route Optimizer
Training
Data
Loss:
Valid Route
Linear Regressor with
Route Validator
????
noise
points

28. Generator
Generator Sample Optimizer
Training
Data
Loss:
Looks Real????
noise
points

29. Generator
Generatornoise Sample Optimizer
Training
Data
Loss:
Looks Real

30. Generator
Generatornoise Sample
Classification
Loss
Training
Data
Learnt Real/Fake
Cost function
Discriminator

31. Generator
Generatornoise Sample
Classification
Loss
Training
Data
Neural Net
Discriminator
Neural Net

32. Generator
Generatornoise Sample
Classification
Loss
Training
Data
Discriminator
Trained via Gradient Descent

33. Generator
Generatornoise Sample
Classification
Loss
Training
Data
Discriminator
Optimizing to fool D

34. Generator
Generatornoise Sample
Classification
Loss
Training
Data
Discriminator
Optimizing to not get fooled by G

38. Route 4

39. Route Crazy!

40. Route Crazy! But valid.

42. Generator
Generator
noise
Sample
Classification
Loss
Training
Data
Discriminator
Optimizing to not get fooled by G
class

45. Generator
Generatornoise Sample
Classification
Loss
Training
Data
Discriminator
Optimizing to not get fooled by G

46. Generator
Generatornoise Sample
Classification
Loss
Training
Data
Discriminator
Optimizing to not get fooled by G
MSE
Loss

53. Uses
• Unsupervised Learning
• Learn when there’s little labeled data
• Planning
• Look-ahead to take better decisions