Deep Networks: Overview
From Ufldl
(→Advantages of deep networks) |
(→Advantages of deep networks) |
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By using a deep network, in the case of images, one can also start to learn part-whole decompositions. | By using a deep network, in the case of images, one can also start to learn part-whole decompositions. | ||
For example, the first layer might learn to group together pixels in an image | For example, the first layer might learn to group together pixels in an image | ||
| - | in order to detect edges. The second layer might then group together edges to | + | in order to detect edges (as seen in the earlier exercises). The second layer might then group together edges to |
| - | detect longer contours, or perhaps simple " | + | detect longer contours, or perhaps detect simple "parts of objects." An even deeper layer |
might then group together these contours or detect even more complex features. | might then group together these contours or detect even more complex features. | ||
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processing. For example, visual images are processed in multiple stages by the | processing. For example, visual images are processed in multiple stages by the | ||
brain, by cortical area "V1", followed by cortical area "V2" (a different part | brain, by cortical area "V1", followed by cortical area "V2" (a different part | ||
| - | of the brain), and so on. | + | of the brain), and so on. |
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