Pooling

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Formally, after obtaining our convolved features as described earlier, we decide the size of the region, say <math>m \times n</math> to pool our convolved features over.  Then, we divide our convolved features into disjoint <math>m \times n</math> 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 <math>m \times n</math> to pool our convolved features over.  Then, we divide our convolved features into disjoint <math>m \times n</math> 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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Latest revision as of 04:17, 8 April 2013

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