CNN 3D line angles prediction regression - results of training of phi depend on theta
In this case changing CNN to a simple MLP with 3 layers changed the behaviour of the network completely. It becomes fast to train, accurate and more importantly, phi results do not depend on the range of theta used during the training (although they depend on particular theta value, as expected). This does not explain why in CNN there is dependency. I guess that convolution filters can't separate parts of the phase space, but I don't have intuition why.