Feature extraction using convolution
From Ufldl
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'''convolve''' them with the larger image, thus obtaining a different feature activation value at each location in the image. | '''convolve''' them with the larger image, thus obtaining a different feature activation value at each location in the image. | ||
| - | To give a concrete example, suppose you have learned features on 8x8 patches sampled from a 96x96 image. To get the convolved features, for every 8x8 region of the 96x96 image, that is, the 8x8 regions starting at <math>(1, 1), (1, 2), \ldots (89, 89)</math>, you would extract the 8x8 patch, and run it through your trained sparse autoencoder to get the feature activations. This would result in | + | To give a concrete example, suppose you have learned features on 8x8 patches sampled from a 96x96 image. Suppose further this was done with an autoencoder that has 100 hidden units. To get the convolved features, for every 8x8 region of the 96x96 image, that is, the 8x8 regions starting at <math>(1, 1), (1, 2), \ldots (89, 89)</math>, you would extract the 8x8 patch, and run it through your trained sparse autoencoder to get the feature activations. This would result in 100 sets 89x89 convolved features. |
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