I have a question regarding the number of filters in a convolutional Autoencoder.
As far as I have understood, as the network gets deeper, the amount of filters in the
convolutional layer increases.
A typical pattern would be to $16, 32, 64, 128, 256, 512 ...$
Right now I am looking into
Autoencoders and on the
Keras Blog I noticed that they do it the other way around.
They start with $16$ filters in the first layer, then the number of filters is decreased:
Now I am wondering if this is usual when working with
Autoencoders or does it depend on what kind of features the network should learn?
Thanks in advance,