池化

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Pooling
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= Pooling =
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Pooling: Overview  
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== Pooling: Overview ==
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After obtaining features using convolution, we would next like to use them for classification. In theory, one could use all the extracted features with a classifier such as a softmax classifier, but this can be computationally challenging. Consider for instance images of size 96x96 pixels, and suppose we have learned 400 features over 8x8 inputs. Each convolution results in an output of size (96 − 8 + 1) * (96 − 8 + 1) = 7921, and since we have 400 features, this results in a vector of 892 * 400 = 3,168,400 features per example. Learning a classifier with inputs having 3+ million features can be unwieldy, and can also be prone to over-fitting.  
After obtaining features using convolution, we would next like to use them for classification. In theory, one could use all the extracted features with a classifier such as a softmax classifier, but this can be computationally challenging. Consider for instance images of size 96x96 pixels, and suppose we have learned 400 features over 8x8 inputs. Each convolution results in an output of size (96 − 8 + 1) * (96 − 8 + 1) = 7921, and since we have 400 features, this results in a vector of 892 * 400 = 3,168,400 features per example. Learning a classifier with inputs having 3+ million features can be unwieldy, and can also be prone to over-fitting.  
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Pooling for Invariance  
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== Pooling for Invariance ==
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If one chooses the pooling regions to be contiguous areas in the image and only pools features generated from the same (replicated) hidden units. Then, these pooling units will then be translation invariant. This means that the same (pooled) feature will be active even when the image undergoes (small) translations. Translation-invariant features are often desirable; in many tasks (e.g., object detection, audio recognition), the label of the example (image) is the same even when the image is translated. For example, if you were to take an MNIST digit and translate it left or right, you would want your classifier to still accurately classify it as the same digit regardless of its final position.  
If one chooses the pooling regions to be contiguous areas in the image and only pools features generated from the same (replicated) hidden units. Then, these pooling units will then be translation invariant. This means that the same (pooled) feature will be active even when the image undergoes (small) translations. Translation-invariant features are often desirable; in many tasks (e.g., object detection, audio recognition), the label of the example (image) is the same even when the image is translated. For example, if you were to take an MNIST digit and translate it left or right, you would want your classifier to still accurately classify it as the same digit regardless of its final position.  
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Formal description
 
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== Formal description ==
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Formally, after obtaining our convolved features as described earlier, we decide the size of the region, say  to pool our convolved features over. Then, we divide our convolved features into disjoint  regions, and take the mean (or maximum) feature activation over these regions to obtain the pooled convolved features. These pooled features can then be used for classification.  
Formally, after obtaining our convolved features as described earlier, we decide the size of the region, say  to pool our convolved features over. Then, we divide our convolved features into disjoint  regions, and take the mean (or maximum) feature activation over these regions to obtain the pooled convolved features. These pooled features can then be used for classification.  
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Revision as of 11:38, 7 March 2013

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