In my idea, deep learning is a process of feature extraction.
Just like multiple layer neural networks (NN):
Input1 => L1 => L2 => ... => Ln => Output1. The special aspect of deep learning is to let
Output1 equal to
Input1. As a result, we can get the error of
Output1. Then, we can try to use backpropagation (BP) to train our model to minimize the error. When it is complete, all layers' output is the internal feature representations from edge to partial of object to full object. This made deep learning so fancy. This concept is illustrated by this picture:
Now, back to convolutional neural networks (CNN). CNN use convolution to extract features and try to learn all filters by BP. I do not see CNN generate the output similar to the input pictures. It is just convolution and pooling and so on to become very small pixel fractions, called basis.
How CNN use deep learning concept in its implementation? Why BP can train CNN model to the correct internal features of all layers?