Pooling

Now that you have obtained an array of convolved features, you might try using these features for classification. However, thinking about why we decided to obtain convolved features suggests a further step that could improve our classification performance. Recall that we decided to obtain convolved features because we thought that the features for the large image would simply be the features for smaller patches translated around the large image. This suggests to us that what we might really be interested in are the feature activations independent of some small translations. You can see why this might be so intuitively - if you were to take an MNIST digit and translate it left or right, you would want your classifier to still accurately classify it as the same digit regardless of its final position.  

Now that you have obtained an array of convolved features, you might try using these features for classification. However, thinking about why we decided to obtain convolved features suggests a further step that could improve our classification performance. Recall that we decided to obtain convolved features because we thought that the features for the large image would simply be the features for smaller patches translated around the large image. This suggests to us that what we might really be interested in are the feature activations independent of some small translations. You can see why this might be so intuitively - if you were to take an MNIST digit and translate it left or right, you would want your classifier to still accurately classify it as the same digit regardless of its final position.