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迁移学习在图像分类中的应用

迁移学习在图像分类中的应用通过复用在大型数据集（如ImageNet）上预训练的深度神经网络骨干（通常是卷积神经网络CNN或视觉Transformer），并将其适配到新的目标域进行图像分类。通过继承源任务的通用视觉特征，该方法能以远少于从头训练所需的标注图像数量达到高精度。

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迁移学习在图像分类中的应用
微调卷积神经网络微调视觉Transformer图像分类基于对象检测的迁移学习域自适应图像分类微调图像分类多语言图像分类半监督图像分类实例分割迁移学习弱监督图像分类

来源

  1. Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI: 10.1109/TKDE.2009.191 ↗
  2. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25. link ↗

如何引用本页

ScholarGate. (2026, June 3). Transfer Learning with Pretrained Deep Neural Networks for Image Classification. ScholarGate. https://scholargate.app/zh/deep-learning/transfer-learning-with-image-classification

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被引用于

域自适应图像分类微调图像分类图像分类多语言图像分类半监督图像分类实例分割迁移学习基于对象检测的迁移学习弱监督图像分类

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ScholarGate — Transfer Learning with Image Classification (Transfer Learning with Pretrained Deep Neural Networks for Image Classification). 于 2026-06-15 检索自 https://scholargate.app/zh/deep-learning/transfer-learning-with-image-classification · 数据集： https://doi.org/10.5281/zenodo.20539026