Introspection: Accelerating Neural Network Training By Learning Weight Evolution
This addresses the issue of slow training for neural network users, but it is incremental as it builds on existing optimization methods.
The paper tackles the problem of long neural network training times by learning weight evolution patterns from a simple network on MNIST and applying them to accelerate training on CIFAR-10 and ImageNet, achieving faster convergence with low memory and computational overhead.
Neural Networks are function approximators that have achieved state-of-the-art accuracy in numerous machine learning tasks. In spite of their great success in terms of accuracy, their large training time makes it difficult to use them for various tasks. In this paper, we explore the idea of learning weight evolution pattern from a simple network for accelerating training of novel neural networks. We use a neural network to learn the training pattern from MNIST classification and utilize it to accelerate training of neural networks used for CIFAR-10 and ImageNet classification. Our method has a low memory footprint and is computationally efficient. This method can also be used with other optimizers to give faster convergence. The results indicate a general trend in the weight evolution during training of neural networks.