卷积神经网络超参数优化的网络结构搜索方法综述

Hudalizaman, I. Ardiyanto, S. Wibirama
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引用次数: 0

摘要

深度学习是一种广泛应用于解决各种问题的方法。用于图像分类的深度学习方法之一是卷积神经网络。卷积神经网络有很多架构。这些架构都是手工制作的,性能优异。然而,由于测试了大量的用例,这导致体系结构必须根据用例进行调整。如果这是手动完成的,那么它应该是耗时的。因此,应该有一种自动的方法来搜索适当的体系结构。神经结构搜索是一种用于搜索合适结构的方法。本文综述了当前研究者对卷积神经网络超参数优化的神经结构搜索方法的研究进展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Network Architecture Search Method on Hyperparameter Optimization of Convolutional Neural Network: Review
Deep Learning is a widely used method to solve various kinds of problems. One of the deep learning methods that are used for image classification is Convolutional Neural Network. Convolutional Neural Network has a lot of architecture. Those architectures were manually made and had excellent performance. However, due to a large number of cases tested, it results in the architecture has to be adjusted to the case. If this is manually done, then it should be time-consuming. So there should be an automatic approach to search the appropriate architecture. Neural Architecture Search is one method that is used to search for the appropriate architecture. This paper is containing reviews of Neural Architecture Search method for hyperparameter optimization on Convolutional Neural Network which is done by today's researchers.
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