Stacked Autoencoders

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(Motivation)
 
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\begin{align}
\begin{align}
a^{(l)} = f(z^{(l)}) \\
a^{(l)} = f(z^{(l)}) \\
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z^{(l + 1)} = W^{(l, 1)}a^{(l)} + b^{(l, l)}
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z^{(l + 1)} = W^{(l, 1)}a^{(l)} + b^{(l, 1)}
\end{align}
\end{align}
</math>
</math>
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===Training===
===Training===
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A good way to obtain good parameters for a stacked autoencoder is to use greedy layer-wise training. To do this, first train the first layer on raw input to obtain parameters W1, W2, b1 and b2. Use the first layer to transform the raw input into a vector consisting of activation of the hidden units, A. Train the second layer on this vector to obtain parameters W1, W2, b1 and b2. Repeat for subsequent layers, using the output of each layer as input for the subsequent layer.
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A good way to obtain good parameters for a stacked autoencoder is to use greedy layer-wise training. To do this, first train the first layer on raw input to obtain parameters <math>W^{(1,1)}, W^{(1,2)}, b^{(1,1)}, b^{(1,2)}</math>. Use the first layer to transform the raw input into a vector consisting of activation of the hidden units, A. Train the second layer on this vector to obtain parameters <math>W^{(2,1)}, W^{(2,2)}, b^{(2,1)}, b^{(2,2)}</math>. Repeat for subsequent layers, using the output of each layer as input for the subsequent layer.
This method trains the parameters of each layer individually while freezing parameters for the remainder of the model. To produce better results, after this phase of training is complete, [[Fine-tuning Stacked AEs | fine-tuning]] using backpropagation can be used to improve the results by tuning the parameters of all layers are changed at the same time.  
This method trains the parameters of each layer individually while freezing parameters for the remainder of the model. To produce better results, after this phase of training is complete, [[Fine-tuning Stacked AEs | fine-tuning]] using backpropagation can be used to improve the results by tuning the parameters of all layers are changed at the same time.  
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Further, it often captures a useful "hierarchical grouping" or "part-whole decomposition" of the input.  To see this, recall that an autoencoder tends to learn features that form a good representation of its input. The first layer of a stacked autoencoder tends to learn first-order features in the raw input (such as edges in an image). The second layer of a stacked autoencoder tends to learn second-order features corresponding to patterns in the appearance of first-order features (e.g., in terms of what edges tend to occur together--for example, to form contour or corner detectors). Higher layers of the stacked autoencoder tend to learn even higher-order features.  
Further, it often captures a useful "hierarchical grouping" or "part-whole decomposition" of the input.  To see this, recall that an autoencoder tends to learn features that form a good representation of its input. The first layer of a stacked autoencoder tends to learn first-order features in the raw input (such as edges in an image). The second layer of a stacked autoencoder tends to learn second-order features corresponding to patterns in the appearance of first-order features (e.g., in terms of what edges tend to occur together--for example, to form contour or corner detectors). Higher layers of the stacked autoencoder tend to learn even higher-order features.  
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{{CNN}}
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For instance, in the context of image input, the first layers usually learns to recognize edges. The second layer usually learns features that arise from combinations of the edges, such as corners. With certain types of network configuration and input modes, the higher layers can learn meaningful combinations of features. For instance, if the input set consists of images of faces, higher layers may learn features corresponding to parts of the face such as eyes, noses or mouths.
For instance, in the context of image input, the first layers usually learns to recognize edges. The second layer usually learns features that arise from combinations of the edges, such as corners. With certain types of network configuration and input modes, the higher layers can learn meaningful combinations of features. For instance, if the input set consists of images of faces, higher layers may learn features corresponding to parts of the face such as eyes, noses or mouths.
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{{Languages|栈式自编码算法|中文}}

Latest revision as of 13:33, 7 April 2013

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