  This paper proposes network recasting as a general method for network
architecture transformation. The primary goal of this method is to accelerate
the inference process through the transformation, but there can be many other
practical applications. The method is based on block-wise recasting; it recasts
each source block in a pre-trained teacher network to a target block in a
student network. For the recasting, a target block is trained such that its
output activation approximates that of the source block. Such a block-by-block
recasting in a sequential manner transforms the network architecture while
preserving the accuracy. This method can be used to transform an arbitrary
teacher network type to an arbitrary student network type. It can even generate
a mixed-architecture network that consists of two or more types of block. The
network recasting can generate a network with fewer parameters and/or
activations, which reduce the inference time significantly. Naturally, it can
be used for network compression by recasting a trained network into a smaller
network of the same type. Our experiments show that it outperforms previous
compression approaches in terms of actual speedup on a GPU.
