Exercise:Convolution and Pooling

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(Step 2: Learn features on small patches)
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=== Step 2: Learn features on small patches ===
=== Step 2: Learn features on small patches ===
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You will now use your sparse autoencoder to learn features on small 8x8 patches sampled from the larger 96x96 STL10 images (The STL10 dataset comprises 5000 test and 8000 train 96x96 labelled color images belonging to one of ten classes: airplane, bird, car, cat, deer, dog, horse, monkey, ship, truck).  
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You will now use your sparse autoencoder to learn features on a set of 100 000 small 8x8 patches sampled from the larger 96x96 STL10 images (The STL10 dataset comprises 5000 test and 8000 train 96x96 labelled color images belonging to one of ten classes: airplane, bird, car, cat, deer, dog, horse, monkey, ship, truck).  
Code has been provided to load patches sampled from the images. Note that you will need to apply the exact same preprocessing steps to the convolved images as you do to the patches used for training the autoencoder (you have to subtract the same mean image and use the exact same whitening matrix), so using a fixed set of patches means that you can recompute these matrices if necessary. Code to load the sampled patches has already been provided, so no additional changes are required on your part.
Code has been provided to load patches sampled from the images. Note that you will need to apply the exact same preprocessing steps to the convolved images as you do to the patches used for training the autoencoder (you have to subtract the same mean image and use the exact same whitening matrix), so using a fixed set of patches means that you can recompute these matrices if necessary. Code to load the sampled patches has already been provided, so no additional changes are required on your part.

Revision as of 07:23, 20 May 2011

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