Hands-On Java Deep Learning for Computer Vision

Pooling characteristics

To summarize once again, the pooling layers always leave the third dimension untouched. We usually misunderstand this with a convolution which reduced 1, but that is not happening with max pooling. Usually pooling layers are used to reduce the first two dimensions because it's quite common to use the stride with them, and they usually do a good job of reducing overfitting.

As you will recall from the convolution, we don't specify the nature of the feature to use. We don't tell the neural network to use vertical, horizontal, Sobel, or Scharr filter; instead, we let the neural network figure out what filter is best for the job it's trying to solve. And this means that the values of the filters are just some parameters ...