Filters in convolutional autoencoders 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:

https://blog.keras.io/building-autoencoders-in-keras.html

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, 

Cheers, 

Michael
Autoencoders are meant to reduce the dimensionality of your data. Increasing the number of filters would do the opposite.