  We propose a set of denoising neural networks capable of operating on an
arbitrary number of channels at runtime, irrespective of how many channels they
were trained on. We coin the proposed models multi-view networks since they
operate using multiple views of the same data. We explore two such
architectures and show how they outperform traditional denoising models in
multi-channel scenarios. Additionally, we demonstrate how multi-view networks
can leverage information provided by additional recordings to make better
predictions, and how they are able to generalize to a number of recordings not
seen in training.
