Since the deep learning model is highly dependent on hyperparameters, hyperparameter optimization is essential in developing deep learning model-based applications, even if it takes a long time. As service development using deep learning models has gradually become competitive, many developers highly demand rapid hyperparameter optimization algorithms. In order to keep pace with the needs of faster hyperparameter optimization algorithms, researchers are focusing on improving the speed of hyperparameter optimization algorithm. However, the huge time consumption of hyperparameter optimization due to the high computational cost of the deep learning model itself has not been dealt with in-depth. Like using surrogate model in Bayesian optimization, to solve this problem, it is necessary to consider proxy model for a neural network (N_B) to be used for hyperparameter optimization. Inspired by the main goal of neural network pruning, i.e., high computational cost reduction and performance preservation, we presumed that the neural network (N_P) obtained through neural network pruning would be a good proxy model of N_B. In order to verify our idea, we performed extensive experiments by using CIFAR10, CFIAR100, and TinyImageNet datasets and three generally-used neural networks and three representative hyperparameter optmization methods. Through these experiments, we verified that N_P can be a good proxy model of N_B for rapid hyperparameter optimization. The proposed hyperparameter optimization framework can reduce the amount of time up to 37%.

翻译：由于深层次学习模型高度依赖于超光谱模型,超光谱优化对于开发深层次学习模型应用程序至关重要,即使需要很长时间。随着使用深层学习模型的服务开发逐渐变得具有竞争力,许多开发者高度要求快速超光谱优化算法。为了跟上更快超光谱优化算法的需求,研究人员正在集中精力提高超光谱优化算法的速度。然而,由于深层学习模型本身的计算成本很高,因此超光谱优化的巨大时间消耗并没有被深入处理。与使用巴伊西亚优化中的代金模型一样,为了解决这一问题,有必要考虑将神经网络(N_B)的代用模型用于超光光谱优化。受神经网络运行的主要目标(即高计算成本降低和性能保护)的启发,我们假设通过神经网络运行的计算成本大幅降低超光谱优化模型(N_P),这可以成为N_B的高级代理模型。为了核实我们的想法,我们通过使用CIFAR10、CFIAR和3个具有代表性的模型进行大规模实验。