I am trying to implement Split brain AutoEncoder paper for understanding and it needs merging. I am not sure I misinterpret the paper, but as per knowledge the author is trying to implement as follows:
finally merge both networks along channels for each layers to test different Classification/Detection network comparisons. e.g. Classification network RGB => (Network 1 + Network 2) => fc5 features => classifier
Now I am not sure how to merge them, if I try to use ‘group’ parameter in convolution to split the network then how do we split 3 channel input so that 1 part of network looks “L” channel and other “ab” channel.
It might need some time to read the paper t answer my query. This is explained in section 3.1/3.2 of paper.
From your description, it seems that you need to transform rgb images into lab space, and activate on each subset of channels separately using the two networks.
yes, we can do that also. But main intention is to use weights learn (feature learning) by spliting the network along channels for each layer and merge both for other tasks and use single network for it
I tried but it for my dataset and network, but transfer learning works poorer as compare to random initalization of weights. So I thought, combining the nets along channel and performing convolution on combined will give more valuable features, I am also not sure of it …So I wanted to try this combining the network and training it for classification.