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Transfer learning is a popular technique for improving the performance ofneural networks. However, existing methods are limited to transferringparameters between networks with same architectures. We present a method fortransferring parameters between neural networks with different architectures.Our method, called DPIAT, uses dynamic programming to match blocks and layersbetween architectures and transfer parameters efficiently. Compared to existingparameter prediction and random initialization methods, it significantlyimproves training efficiency and validation accuracy. In experiments onImageNet, our method improved validation accuracy by an average of 1.6 timesafter 50 epochs of training. DPIAT allows both researchers and neuralarchitecture search systems to modify trained networks and reuse knowledge,avoiding the need for retraining from scratch. We also introduce a networkarchitecture similarity measure, enabling users to choose the best sourcenetwork without any training.

Breaking the Architecture Barrier: A Method for Efficient Knowledge Transfer Across Networks