Category Archives: Videos and Talks

Intro. to Contractive Auto-Encoders

Contractive Auto-Encoder is a variation of well-known Auto-Encoder algorithm that has a solid background in the information theory and lately deep learning community. The simple Auto-Encoder targets to compress information of the given data as keeping the reconstruction cost lower as much as possible. However another use is to enlarge the given input's representation. In that case, you learn over-complete representation of the given data instead of compressing it. Most common implication is Sparse Auto-Encoder that learns over-complete representation but in a sparse (smart) manner. That means, for a given instance only informative set of units are activated, therefore you are able to capture more discriminative representation, especially if you use AE for pre-training of your deep neural network.

After this intro. what is special about Contraction Auto-Encoder (CAE)?  CAE simply targets to learn invariant representations to unimportant transformations for the given data. It only learns transformations that are exactly in the given dataset and try to avoid more. For instance, if you have set of car images and they have left and right view points in the dataset, then CAE is sensitive to those changes but it is insensitive to frontal view point. What it means that if you give a frontal car image to CAE after the training phase, it tries to contract its representation to one of the left or right view point car representation at the hidden layer. In that way you obtain some level of view point in-variance. (I know, this is not very good example for a cannier guy but I only try to give some intuition for CAE).

From the mathematical point of view, it gives the effect of contraction by adding an additional term to reconstruction cost. This addition is the Sqrt Frobenius norm of Jacobian of the hidden layer representation with respect to input values. If this value is zero, it means, as we change input values, we don't observe any change on the learned hidden representations. If we get very large values then the learned representation is unstable as the input values change.

This was just a small intro to CAE, if you like the idea please follow the below videos of Hugo Larochelle's lecture and Pascal Vincent's talk at ICML 2011 for the paper.

 

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Here is the G. Hinton's talk at MIT about t inabilities of Convolutional Neural Networks and 4 basic arguments to solve these.

I just watched it with a slight distraction and I need to reiterate. However these are the basic arguments in which G. Hinton is proposed whilst the speech.

1.  CNN + Max Pooling is not the way of handling visual information as the human brain does. Yes, it works in practice for the current state of the art but, especially view point changes of the target objects are still unsolved.

2. Apply Equivariance instead of Invariance. Instead of learning invariant representations to the view point changes, learn changing representations correlated with the view point changes.

3. In the space of CNN weight matrices, view point changes are totally non-linear and therefore hard to learn. However, if we transfer instances into a space where the view point changes are globally linear, we can ease the problem. ( Use graphics representation uses explicit pose coordinates)

4. Route information to right set of neurons instead of unguided forward and backward passes. Define certain neuron groups ( called capsules ) that are receptive to  particular set of data clusters in the instance space and each of these capsules contributes to the whole model as much as the given instance's membership to neuron's cluster.

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