Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.

Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.

Successfully reported this slideshow.

Like this presentation? Why not share!

- WhosOn live Chat - Analytics, Inter... by ianrowley 1377 views
- Predictive coding : inhibition in t... by Jérémie Kalfon 56 views
- 2010 deep learning and unsupervised... by Van Thanh 1158 views
- Pred netにおけるl1lossとl2lossの比較 by koji ochiai 590 views
- Pred net使ってみた by koji ochiai 1878 views
- GRU-Prednetを実装してみた(途中経過) by taichi iki 1558 views

971 views

Published on

Published in:
Science

No Downloads

Total views

971

On SlideShare

0

From Embeds

0

Number of Embeds

195

Shares

0

Downloads

21

Comments

0

Likes

2

No embeds

No notes for slide

- 1. Article overview by Ilya Kuzovkin William Lotter, Gabriel Kreiman & David Cox Computational Neuroscience Seminar University of Tartu 2015 Harvard University, Cambridge, USA Unsupervised Learning of Visual Structure Using Predictive Generative Networks
- 2. The idea of predictive coding in neuroscience
- 3. “state-of-the-art deep learning models rely on millions of labeled training examples to learn”
- 4. “state-of-the-art deep learning models rely on millions of labeled training examples to learn” “in contrast to biological systems, where learning is largely unsupervised”
- 5. “state-of-the-art deep learning models rely on millions of labeled training examples to learn” “in contrast to biological systems, where learning is largely unsupervised” “we explore the idea that prediction is not only a useful end-goal, but may also serve as a powerful unsupervised learning signal”
- 6. PART I THE IDEA OF PREDICTIVE ENCODER "prediction may also serve as a powerful unsupervised learning signal"
- 7. PREDICTIVE GENERATIVE NETWORK (a.k.a “Predictive Encoder” Palm 2012) vs.
- 8. input output “bottleneck” AUTOENCODER
- 9. input output “bottleneck” AUTOENCODER
- 10. input output “bottleneck” AUTOENCODER
- 11. input output “bottleneck” Reconstruction AUTOENCODER
- 12. input output “bottleneck” Reconstruction AUTOENCODER
- 13. input output “bottleneck” Can we do prediction? Reconstruction AUTOENCODER
- 14. PREDICTIVE GENERATIVE NETWORK (a.k.a “Predictive Encoder” Palm 2012) vs.
- 15. RECURRENT NEURAL NETWORK
- 16. RECURRENT NEURAL NETWORK
- 17. RECURRENT NEURAL NETWORK
- 18. RECURRENT NEURAL NETWORK
- 19. PREDICTIVE GENERATIVE NETWORK (a.k.a “Predictive Encoder” Palm 2012) vs.
- 20. PREDICTIVE GENERATIVE NETWORK (a.k.a “Predictive Encoder” Palm 2012) vs. Convolution ReLu Max-pooling 2x {
- 21. PREDICTIVE GENERATIVE NETWORK (a.k.a “Predictive Encoder” Palm 2012) vs. Long Short-Term Memory (LSTM) 5 - 15 steps 1024 units Convolution ReLu Max-pooling 2x {
- 22. PREDICTIVE GENERATIVE NETWORK (a.k.a “Predictive Encoder” Palm 2012) vs. Long Short-Term Memory (LSTM) 5 - 15 steps 1024 units Convolution ReLu Max-pooling 2x { 2 layers NN upsampling ReLu Convolution
- 23. PREDICTIVE GENERATIVE NETWORK (a.k.a “Predictive Encoder” Palm 2012) vs. Long Short-Term Memory (LSTM) 5 - 15 steps 1024 units Convolution ReLu Max-pooling 2x { MSE loss RMSProp optimizer LR 0.001 2 layers NN upsampling ReLu Convolution
- 24. PREDICTIVE GENERATIVE NETWORK (a.k.a “Predictive Encoder” Palm 2012) vs. Long Short-Term Memory (LSTM) 5 - 15 steps 1024 units http://keras.io 2 layers NN upsampling Convolution ReLu ReLu Convolution Max-pooling 2x { MSE loss RMSProp optimizer LR 0.001
- 25. PART II ADVERSARIAL LOSS "the generator is trained to maximally confuse the adversarial discriminator"
- 26. vs. Long Short-Term Memory (LSTM) 5 - 15 steps 1568 units Fully connected layer 2 layers NN upsampling Convolution ReLu ReLu Convolution Max-pooling 2x{ MSE loss RMSProp optimizer LR 0.001
- 27. vs. Long Short-Term Memory (LSTM) 5 - 15 steps 1568 units Fully connected layer 2 layers NN upsampling Convolution ReLu ReLu Convolution Max-pooling 2x{ MSE loss RMSProp optimizer LR 0.001
- 28. MSE loss
- 29. MSE loss
- 30. MSE loss
- 31. 3 FC layers (relu, relu, softmax) MSE loss
- 32. 3 FC layers (relu, relu, softmax) "trained to maximize the probability that a proposed frame came from the ground truth data and minimize it when it is produced by the generator" MSE loss
- 33. 3 FC layers (relu, relu, softmax) "trained to maximize the probability that a proposed frame came from the ground truth data and minimize it when it is produced by the generator" AL loss to train PGN MSE loss AL loss
- 34. 3 FC layers (relu, relu, softmax) "trained to maximize the probability that a proposed frame came from the ground truth data and minimize it when it is produced by the generator" AL loss to train PGN MSE loss AL loss
- 35. “with adversarial loss alone the generator easily found solutions that fooled the discriminator, but did not look anything like the correct samples” MSE model is fairly faithful to the identities of the faces, but produces blurred versions combined AL/MSE model tends to underﬁt the identity towards a more average face
- 36. PART III INTERNAL REPRESENTATIONS AND LATENT VARIABLES "we are interested in understanding the representations learned by the models"
- 37. PGN model LSTM activities L2 regression Value of a latent variable
- 38. PGN model LSTM activities L2 regression Value of a latent variable
- 39. “An MDS algorithm aims to place each object in N-dimensional space such that the between-object distances are preserved as well as possible.” MULTIDIMENSIONAL SCALING
- 40. PART IV USEFULNESS OF PREDICTIVE LEARNING "representations trained with a predictive loss outperform other models of comparable complexity in a supervised classiﬁcation problem"
- 41. THE TASK: 50 randomly generated faces (12 angles per each) Generative models: Internal representation SVM Identify class
- 42. THE TASK: 50 randomly generated faces (12 angles per each) Generative models: Internal representation SVM Identify class • Encoder-LSTM-Decoder to predict next frame (PGN) • Encoder-LSTM-Decoder to predict last frame (AE LSTM dynamic) • Encoder-LSTM-Decoder on frames made into static movies (AE LSTM static) • Encoder-FC-Decoder with #weights as in LSTM (AE FC #weights) • Encoder-FC-Decoder with #units as in LSTM (AE FC #units)

No public clipboards found for this slide

Be the first to comment