Network Iterative Learning for Dynamic Deep Neural Networks via Morphism
In this research, we present a novel learning scheme called network iterative learning for deep neural networks. Different from traditional optimization algorithms that usually optimize directly on a static objective function, we propose in this work to optimize a dynamic objective function in an iterative fashion capable of adapting its function form when being optimized. The optimization is implemented as a series of intermediate neural net functions that is able to dynamically grow into the targeted neural net objective function. This is done via network morphism so that the network knowledge is fully preserved with each network growth. Experimental results demonstrate that the proposed network iterative learning scheme is able to significantly alleviate the degradation problem. Its effectiveness is verified on diverse benchmark datasets.
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CIFAR-10
CIFAR-100
