A Path To Unsupervised Learning Through Adversarial Networks - (Soumith Chintala, Researcher at Facebook AI Research) Soumith Chintala is a Researcher at Facebook AI Research, where he works on deep learning, reinforcement learning, generative image models, agents for video games and large-scale high-performance deep learning. He holds a Masters in CS from NYU, and spent time in Yann LeCun's NYU lab building deep learning models for pedestrian detection, natural image OCR, depth-images among others. Soumith will go over generative adversarial networks, a particular way of training neural networks to build high quality generative models. The talk will take you through an easy to follow timeline of the research and improvements in adversarial networks, followed by some future directions, as well as applications.

1. A path to unsupervised learning
Soumith Chintala
Facebook AI Research
through Adversarial Networks

2. Overview
of the talk
• Unsupervised Learning
• Generative Adversarial Networks
• Advances
• Using the learnt representations
• What’s next?

3. Unsupervised Learning
An introduction

4. Unsupervised Learning
An introduction
Supervised Learning

5. Unsupervised Learning
An introduction
Unsupervised Learning

6. Unsupervised Learning
Usefulness

7. Unsupervised Learning
Reusing representations

8. Generative Models
An introduction
A model that learns a distribution of images

9. Generative Models
An introduction
X = P(z), z controls dogness or catness

10. Generative Models
An introduction
X = P(z), z is a latent variable

11. Generative Models
An introduction
P(z) = neural network

12. Generative Adversarial Networks

13. Generative Adversarial Networks
Alternating optimization
Generator Sample Optimizer
Training
Data
Loss:
Looks Real

14. Generative Adversarial Networks

15. Generative Adversarial Networks
Alternating optimization
Generatornoise Sample
Classification
Loss
Training
Data
Learnt Real/Fake
Cost function
Discriminator

16. Generative Adversarial Networks
Alternating optimization
Generatornoise Sample
Classification
Loss
Training
Data
Discriminator
Trained via Gradient Descent

17. Generative Adversarial Networks
Alternating optimization
Generatornoise Sample
Classification
Loss
Training
Data
Discriminator
Optimizing to fool D

18. Generative Adversarial Networks
Alternating optimization
Generatornoise Sample
Classification
Loss
Training
Data
Discriminator
Optimizing to not get fooled by G

19. Generative Adversarial Networks
Optimizes Jensen-Shannon Divergence

20. Generative Adversarial Networks
Samples

21. Class-conditional GANs

22. Generator
noise
Sample
Classification
Loss
Training
Data
Discriminator
Class-conditional GANs
Not unsupervised
class

24. Video Prediction GANs

25. Generatornoise Sample
Classification
Loss
Training
Data
Discriminator
Video Prediction GANs

26. Generatornoise Sample
Classification
Loss
Training
Data
Discriminator
Video Prediction GANs
Generatornoise Sample
Classification
Loss
Training
Data
Discriminator
MSE
Loss

27. Video Prediction GANs

28. DCGANs

31. Latent space arithmetic

32. Using the GAN feature representation

33. Using the GAN feature representation

34. Using the GAN feature representation
Needs much lesser labeled data

35. Using the GAN feature representation

36. In-painting GANs

37. In-painting GANs

38. In-painting GANs

39. Disentangling representations

40. Disentangling representations

41. Disentangling representations

42. Disentangling representations

43. Disentangling representations

44. Disentangling representations

45. Stability and Representation Reuse

46. Stability and Representation Reuse
• Feature matching
• Minibatch discrimination
• Label smoothing
• What’s next?

47. Stability and Representation Reuse

48. Stability and Representation Reuse

49. What’s next?
• Planning and forward modeling

50. Questions
• When will adversarial networks take over the world?
• Soon.