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微调卷积神经网络

微调卷积神经网络（CNN）是指从一个已经在大型数据集（通常是ImageNet）上训练过的网络开始，然后在较小的目标数据集上继续训练，使模型能够适应新任务的视觉特征。与从头开始训练相比，这种方法大大减少了达到高性能所需的数据和计算量。

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微调卷积神经网络
微调循环神经网络微调视觉Transformer图像分类目标检测基于卷积神经网络的迁移学习域自适应卷积神经网络微调生成对抗网络微调图像分类微调多层感知机微调语义分割

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来源

  1. Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? Advances in Neural Information Processing Systems, 27. link ↗
  2. Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., & Liang, J. (2016). Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE Transactions on Medical Imaging, 35(5), 1299–1312. DOI: 10.1109/TMI.2016.2535302 ↗

如何引用本页

ScholarGate. (2026, June 3). Fine-Tuned Convolutional Neural Network (CNN Fine-Tuning via Transfer Learning). ScholarGate. https://scholargate.app/zh/deep-learning/fine-tuned-convolutional-neural-network

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被引用于

域自适应卷积神经网络微调生成对抗网络微调图像分类微调多层感知机微调语义分割微调变分自编码器微调视觉Transformer自监督卷积神经网络半监督卷积神经网络基于卷积神经网络的迁移学习迁移学习在图像分类中的应用基于对象检测的迁移学习弱监督卷积神经网络

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ScholarGate — Fine-Tuned Convolutional Neural Network (Fine-Tuned Convolutional Neural Network (CNN Fine-Tuning via Transfer Learning)). 于 2026-06-15 检索自 https://scholargate.app/zh/deep-learning/fine-tuned-convolutional-neural-network · 数据集： https://doi.org/10.5281/zenodo.20539026