Machine learningDeep learning / NLP / CV
微调图像分类
微调图像分类通过在有标签的目标域图像上继续训练，来调整在大规模通用图像语料库（如 ImageNet）上预训练的神经网络，使其适应特定目标域。与从头开始训练相比，这种方法能以少得多的目标域样本实现高准确率，因此成为应用计算机视觉任务的主流范式。
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Method map
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来源
- Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? Advances in Neural Information Processing Systems (NeurIPS), 27, 3320–3328. link ↗
- 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 ↗
如何引用本页
ScholarGate. (2026, June 3). Fine-Tuned Deep Neural Network for Image Classification. ScholarGate. https://scholargate.app/zh/deep-learning/fine-tuned-image-classification
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- 微调卷积神经网络深度学习↔ compare
- 微调视觉Transformer深度学习↔ compare
- 图像分类深度学习↔ compare
- 目标检测深度学习↔ compare
- 迁移学习在图像分类中的应用深度学习↔ compare