I have been looking at autoencoders from the keras blog here: https://blog.keras.io/building-autoencoders-in-keras.html
I was wondering, what motifications would be necessary in order to be able to give it different surfaces i.e. 2-dimensional vectors, of which some of them have large spikes.
For example here we see a surface that looks clean:
How could a neural network look like, if I wanted to remove individual spikes from this surface?
Am I right in thinking that a normal fully connected feed forward propagation would be sufficient? If so, is there any way to control thresholds when spikes are should be eliminated?
Also, would you agree that the training principle would still be the same as show in the keras blog? Would it work if I simply trained it with many good examples of clean surfaces to recognize themselves?