Stacked Autoencoders
From Ufldl
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For instance, in the context of image input, the first layers usually learns to recognize edges. The second layer usually learns features that arise from combinations of the edges, such as corners. With certain types of network configuration and input modes, the higher layers can learn meaningful combinations of features. For instance, if the input set consists of images of faces, higher layers may learn features corresponding to parts of the face such as eyes, noses or mouths. | For instance, in the context of image input, the first layers usually learns to recognize edges. The second layer usually learns features that arise from combinations of the edges, such as corners. With certain types of network configuration and input modes, the higher layers can learn meaningful combinations of features. For instance, if the input set consists of images of faces, higher layers may learn features corresponding to parts of the face such as eyes, noses or mouths. | ||
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