  Transfer learning is a popular technique for improving the performance of
neural networks. However, existing methods are limited to transferring
parameters between networks with same architectures. We present a method for
transferring parameters between neural networks with different architectures.
Our method, called DPIAT, uses dynamic programming to match blocks and layers
between architectures and transfer parameters efficiently. Compared to existing
parameter prediction and random initialization methods, it significantly
improves training efficiency and validation accuracy. In experiments on
ImageNet, our method improved validation accuracy by an average of 1.6 times
after 50 epochs of training. DPIAT allows both researchers and neural
architecture search systems to modify trained networks and reuse knowledge,
avoiding the need for retraining from scratch. We also introduce a network
architecture similarity measure, enabling users to choose the best source
network without any training.
