论文标题
通过广义taguchi方法和目标向量规范的多目标鲁棒CNN系统的最佳超参数和结构设置
Optimal Hyperparameters and Structure Setting of Multi-Objective Robust CNN Systems via Generalized Taguchi Method and Objective Vector Norm
论文作者
论文摘要
最近，机器学习（ML），人工智能（AI）和卷积神经网络（CNN）在广泛的应用中取得了巨大进展，在该应用程序中，其系统具有深度学习结构以及大量确定CNN和AI系统质量和性能的超级参数。这些系统可能具有多目标ML和AI性能需求。有一个关键要求，要找到多目标强大最佳CNN系统的最佳超参数和结构。本文提出了一种广义的Taguchi方法，以通过其目标性能向量范围有效地确定多目标可靠的最佳CNN系统的最佳超参数和结构。提出的方法和方法应用于CNN分类系统，其原始RESNET作为CIFAR-10数据集作为演示和验证，这表明所提出的方法非常有效，可以实现CIFAR-10上原始Resnet的最佳精度。
Recently, Machine Learning (ML), Artificial Intelligence (AI), and Convolutional Neural Network (CNN) have made huge progress with broad applications, where their systems have deep learning structures and a large number of hyperparameters that determine the quality and performance of the CNNs and AI systems. These systems may have multi-objective ML and AI performance needs. There is a key requirement to find the optimal hyperparameters and structures for multi-objective robust optimal CNN systems. This paper proposes a generalized Taguchi approach to effectively determine the optimal hyperparameters and structure for the multi-objective robust optimal CNN systems via their objective performance vector norm. The proposed approach and methods are applied to a CNN classification system with the original ResNet for CIFAR-10 dataset as a demonstration and validation, which shows the proposed methods are highly effective to achieve an optimal accuracy rate of the original ResNet on CIFAR-10.
