Simple Introduction to AutoEncoder

1. Simple Introduction to
AutoEncoder
Lang Jun
Deep Learning Study Group, HLT, I2R
17 August, 2012

2. Outline
1. What is AutoEncoder?
Input = decoder(encoder(input))
2. How to train AutoEncoder?
pre-training
3. What can it be used for?
reduce dimensionality
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3. 1. What is AutoEncoder?
➢ Multilayer neural net simple review
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4. 1. What is AutoEncoder?
➢ Multilayer neural net simple review
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5. 1. What is AutoEncoder?
➢ Multilayer neural net simple review
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6. 1. What is AutoEncoder?
➢ Multilayer neural net simple review
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7. 1. What is AutoEncoder?
➢ Multilayer neural net simple review
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8. 1. What is AutoEncoder?
➢ Multilayer neural net simple review
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9. 1. What is AutoEncoder?
➢ Multilayer neural net simple review
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10. 1. What is AutoEncoder?
➢ Multilayer neural net simple review
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11. 1. What is AutoEncoder?
➢ Multilayer neural net simple review
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12. 1. What is AutoEncoder?
➢ Multilayer neural net simple review
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13. 1. What is AutoEncoder?
➢ Multilayer neural net simple review
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14. 1. What is AutoEncoder?
➢ Multilayer neural net simple review
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15. 1. What is AutoEncoder?
➢ Multilayer neural net simple review
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16. 1. What is AutoEncoder?
➢ Multilayer neural net simple review
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17. 1. What is AutoEncoder?
➢ Multilayer neural net simple review
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18. 1. What is AutoEncoder?
➢ Multilayer neural net simple review
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19. 1. What is AutoEncoder?
➢ Multilayer neural net simple review
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20. 1. What is AutoEncoder?
➢ Multilayer neural net simple review
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21. 1. What is AutoEncoder?
➢ Multilayer neural net simple review
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22. 1. What is AutoEncoder?
➢ Multilayer neural net simple review
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23. 1. What is AutoEncoder?
➢ Multilayer neural net simple review
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24. 1. What is AutoEncoder?
➢ Multilayer neural net with target output = input
➢ Reconstruction=decoder(encoder(input))
➢ Minimizing reconstruction error
➢ Probable inputs have small reconstruction error
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25. 2. How to train AutoEncoder?
Hinton (2006) Science Paper
Restricted Boltzmann Machine
(RBM)
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26. 2. How to train AutoEncoder?
Hinton (2006) Science Paper
restricted Boltzmann machine
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27. Effective deep learning became
possible through unsupervised pre-
training
Purely supervised neural net With unsupervised pre‐training
(with RBMs and Denoising Auto-Encoders)
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0–9 handwritten digit recognition error rate (MNIST data)

28. Why is unsupervised pre-training working so well?
Regularization hypothesis:
Representations good
for P(x) are good for P(y|x)
Optimization hypothesis:
Unsupervised initializations
start near better local minimum
of supervised training error
Minima otherwise not
achievable by random
initialization
Erhan, Courville, Manzagol, Vincent, Bengio (JMLR, 2010)
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29. 3. What can it be used for?
illustration for images
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30. 3. What can it be used for?
document retrieval
output
2000 reconstructed counts vector
• We train the neural network
500 neurons to reproduce its input vector
as its output
• This forces it to compress as
250 neurons much information as possible
into the 10 numbers in the
central bottleneck.
10 • These 10 numbers are then a
good way to compare
documents.
250 neurons
– See Ruslan
Salakhutdinov’s talk
500 neurons
input 30/34
2000 word counts vector

31. 3. What can it be used for?
visualize documents
output
2000 reconstructed counts vector
• Instead of using codes to
retrieve documents, we can 500 neurons
use 2-D codes to visualize sets
of documents.
– This works much better 250 neurons
than 2-D PCA
2
250 neurons
500 neurons
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2000 word counts vector

32. First compress all documents to 2 numbers using a type of PCA
Then use different colors for different
document categories
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33. First compress all documents to 2 numbers with an autoencoder
Then use different colors for different document
categories
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34. 3. What can it be used for?
transliteration
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35. Thanks for your attendance
Looking forward to present
Recursive AutoEncoder
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