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Neural Networks are function approximators that have achievedstate-of-the-art accuracy in numerous machine learning tasks. In spite of theirgreat success in terms of accuracy, their large training time makes itdifficult to use them for various tasks. In this paper, we explore the idea oflearning weight evolution pattern from a simple network for acceleratingtraining of novel neural networks. We use a neural network to learn thetraining pattern from MNIST classification and utilize it to acceleratetraining of neural networks used for CIFAR-10 and ImageNet classification. Ourmethod has a low memory footprint and is computationally efficient. This methodcan also be used with other optimizers to give faster convergence. The resultsindicate a general trend in the weight evolution during training of neuralnetworks.

Introspection: Accelerating Neural Network Training By Learning Weight Evolution