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微调变分自编码器

微调变分自编码器（Fine-Tuned Variational Autoencoder）始于一个在大型源数据集上预训练过的VAE，然后继续在较小的目标域数据集上进行训练。此方法将学习到的潜在表示和生成能力适配到新数据，在保持通用结构的同时专门化于目标分布——当标记或大规模目标数据稀缺时，其效果优于从头开始训练。

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微调变分自编码器
微调卷积神经网络微调扩散模型微调生成对抗网络微调Transformer迁移学习与变分自编码器变分自编码器可解释变分自编码器自监督变分自编码器

来源

  1. Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. In Proceedings of the 2nd International Conference on Learning Representations (ICLR 2014). link ↗
  2. 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 Variational Autoencoder (Domain-Adapted VAE). ScholarGate. https://scholargate.app/zh/deep-learning/fine-tuned-variational-autoencoder

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被引用于

可解释变分自编码器微调扩散模型微调生成对抗网络自监督变分自编码器迁移学习与变分自编码器

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ScholarGate — Fine-Tuned Variational Autoencoder (Fine-Tuned Variational Autoencoder (Domain-Adapted VAE)). 于 2026-06-15 检索自 https://scholargate.app/zh/deep-learning/fine-tuned-variational-autoencoder · 数据集： https://doi.org/10.5281/zenodo.20539026