Autoencoder neural networks consist of two main parts:
- The first part is called the encoder, which reduces the dimensions of the input data. Generally, this is an image. When data from an input image is passed through a network that leads to a lower dimension, the network is forced to extract only the most important features of the input data.
- The second part of the autoencoder is called the decoder and it tries to reconstruct the original data from whatever is available from the output of the encoder. The autoencoder network is trained by specifying what output this network should try to match.
Let's consider some examples where we will use image data. If the output that's specified is the same image that was given as input, then after training, the autoencoder network is expected to provide an image with a lower resolution that retains the key...