图像分类的深度学习方法

Yan Yu
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引用次数: 1

摘要

与传统的机器学习算法相比,深度学习模型可以获得更高的精度结果。它在各个领域都有广泛的应用,尤其是在图像分类领域。近年来,由于硬件的改进和新的深度学习网络结构的发现,用于图像分类的深度学习模型的准确性和可靠性有了很大的提高。然而,在深度学习图像分类领域,近年来的研究综述较少。本文对近年来基于深度学习的图像分类研究进行了综述。它包括了关于提高深度学习性能的最新研究。并对深度学习技术可能存在的问题和挑战以及未来可能的改进和研究方向进行了分析和讨论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning Approaches for Image Classification
Deep learning models can achieve a higher accuracy result compared with traditional machine learning algorithm. It is widely useful in different areas, especially in images classification area. In recent years, because of the improvement of hardware and the discovery of new deep learning network structures, the accuracy and reliability of deep learning model used in image classification have been greatly improved. However, in the field of images classification with deep learning technology, the reviews of the recent researches are lack. This paper will make a review about the recent researches of images classification based on deep learning. It includes the latest studies to improve the performance about deep learning. Additionally, the potential problems and challenges on deep learning technology and the possible future improvement and research direction are analyzed and discussed in the review.
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