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This paper proposes network recasting as a general method for networkarchitecture transformation. The primary goal of this method is to acceleratethe inference process through the transformation, but there can be many otherpractical applications. The method is based on block-wise recasting; it recastseach source block in a pre-trained teacher network to a target block in astudent network. For the recasting, a target block is trained such that itsoutput activation approximates that of the source block. Such a block-by-blockrecasting in a sequential manner transforms the network architecture whilepreserving the accuracy. This method can be used to transform an arbitraryteacher network type to an arbitrary student network type. It can even generatea mixed-architecture network that consists of two or more types of block. Thenetwork recasting can generate a network with fewer parameters and/oractivations, which reduce the inference time significantly. Naturally, it canbe used for network compression by recasting a trained network into a smallernetwork of the same type. Our experiments show that it outperforms previouscompression approaches in terms of actual speedup on a GPU.

Network Recasting: A Universal Method for Network Architecture Transformation