Self-Taught Learning
From Ufldl
(→On the terminology of unsupervised feature learning) |
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(perhaps with appropriate whitening or other pre-processing): | (perhaps with appropriate whitening or other pre-processing): | ||
| - | [[File:STL_SparseAE.png]] | + | [[File:STL_SparseAE.png|350px]] |
Having trained the parameters <math>\textstyle W^{(1)}, b^{(1)}, W^{(2)}, b^{(2)}</math> of this model, | Having trained the parameters <math>\textstyle W^{(1)}, b^{(1)}, W^{(2)}, b^{(2)}</math> of this model, | ||
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neural network: | neural network: | ||
| - | [[File:STL_SparseAE_Features.png]] | + | [[File:STL_SparseAE_Features.png|300px]] |
This is just the sparse autoencoder that we previously had, with with the final | This is just the sparse autoencoder that we previously had, with with the final | ||
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get a database of images where every image is either a car or a motorcycle, but | get a database of images where every image is either a car or a motorcycle, but | ||
just missing its label?), and so in the context of learning features from unlabeled | just missing its label?), and so in the context of learning features from unlabeled | ||
| - | data, the self-taught learning setting is | + | data, the self-taught learning setting is more broadly applicable. |
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| + | {{STL}} | ||
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| + | {{Languages|自我学习|中文}} | ||
