Feature extraction using convolution
From Ufldl
(→Locally Connected Networks) |
(→Weight Sharing (Convolution)) |
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This idea of having locally connected networks also draws inspiration from how the early visual system is wired up in biology. Specifically, neurons in the visual cortex have localized receptive fields (i.e., they respond only to stimuli in a certain location). | This idea of having locally connected networks also draws inspiration from how the early visual system is wired up in biology. Specifically, neurons in the visual cortex have localized receptive fields (i.e., they respond only to stimuli in a certain location). | ||
| - | == | + | == Convolutions == |
Natural images have the property of being '''stationary''', meaning that the statistics of one part of the image are the same as any other part. This suggests that the features that we learn at one part of the image can also be applied to other regions--i.e., we can use the same features at all locations. | Natural images have the property of being '''stationary''', meaning that the statistics of one part of the image are the same as any other part. This suggests that the features that we learn at one part of the image can also be applied to other regions--i.e., we can use the same features at all locations. | ||
