Quem foi que disse que não podem ocorrer alterações morfológicas nas arquiteturas/topologias de Redes Neurais?
Abstract: 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.
Conclusions: This paper presented a systematic study on the problem of network morphism at a higher level, and tried to answer the central question of such learning scheme, i.e., whether and how a convolutional layer can be morphed into an arbitrary module. To facilitate the study, we abstracted a modular network as a graph, and formulated the process of network morphism as a graph transformation process. Based on this formulation, both simple morphable modules and complex modules have been defined and corresponding morphing algorithms have been proposed. We have shown that a convolutional layer can be morphed into any module of a network. We have also carried out experiments to illustrate how to achieve a better performing model based on the state-of-the-art ResNet with minimal extra computational cost on benchmark datasets.