Modularized Morphing of Neural Networks

In this work we study the problem of network morphism, an effective learning
scheme to morph a well-trained neural network to a new one with the network
function completely preserved. Different from existing work where basic morphing
types on the layer level were addressed, we target at the central problem of network
morphism at a higher level, i.e., how a convolutional layer can be morphed
into an arbitrary module of a neural network. To simplify the representation of a
network, we abstract a module as a graph with blobs as vertices and convolutional
layers as edges, based on which the morphing process is able to be formulated as a
graph transformation problem. Two atomic morphing operations are introduced to
compose the graphs, based on which modules are classified into two families, i.e.,
simple morphable modules and complex modules. We present practical morphing
solutions for both of these two families, and prove that any reasonable module can
be morphed from a single convolutional layer. Extensive experiments have been
conducted based on the state-of-the-art ResNet on benchmark datasets, and the
effectiveness of the proposed solution has been verified.