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发布时间：2025年09月27日作者:aiycxz.cn

题目基于深度学习的图像分类算法研究学院计算机科学与技术学院专业计算机科学与技术学生姓名张三学号 202001010101指导教师李四教授二〇二四年五月# 基于深度学习的图像分类算法研究## 摘要随着人工智能技术的快速发展，深度学习在图像分类领域取得了显著成果。本文旨在研究基于深度学习的图像分类算法，探讨其原理、方法及应用。首先，介绍了深度学习的基本概念和图像分类的背景，阐述了卷积神经网络（CNN）在图像分类中的核心作用。其次，详细分析了经典的CNN模型，如LeNet、AlexNet、VGG、GoogLeNet和ResNet，比较了它们的结构和性能。然后，针对图像分类中的关键问题，如数据增强、模型优化和迁移学习，提出了相应的解决方案。最后，通过实验验证了所提方法的有效性，并展望了未来研究方向。**关键词：** 深度学习；图像分类；卷积神经网络；数据增强；迁移学习---# Research on Image Classification Algorithms Based on Deep Learning## AbstractWith the rapid development of artificial intelligence technology, deep learning has achieved remarkable results in the field of image classification. This paper aims to study image classification algorithms based on deep learning, exploring their principles, methods, and applications. Firstly, the basic concepts of deep learning and the background of image classification are introduced, and the core role of convolutional neural networks (CNN) in image classification is elaborated. Secondly, classic CNN models such as LeNet, AlexNet, VGG, GoogLeNet, and ResNet are analyzed in detail, and their structures and performances are compared. Then, corresponding solutions are proposed for key issues in image classification, such as data augmentation, model optimization, and transfer learning. Finally, the effectiveness of the proposed methods is verified through experiments, and future research directions are prospected.**Keywords:** Deep Learning; Image Classification; Convolutional Neural Network; Data Augmentation; Transfer Learning