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迁移学习与变分自编码器

迁移学习与变分自编码器（TL-VAE）重用在大型源数据集上预训练的编码器和/或解码器，并将其适配到较小的目标域。通过继承丰富的概率潜在空间，而不是从随机权重开始，TL-VAE 大大减少了高质量生成或表示学习所需的目标域数据量。

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迁移学习与变分自编码器
微调生成对抗网络微调变分自编码器生成对抗网络半监督变分自编码器基于卷积神经网络的迁移学习变分自编码器多语言变分自编码器

来源

  1. Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. 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). Transfer Learning with Variational Autoencoder. ScholarGate. https://scholargate.app/zh/deep-learning/transfer-learning-variational-autoencoder

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被引用于

微调变分自编码器多语言变分自编码器半监督变分自编码器

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ScholarGate — Transfer learning variational autoencoder (Transfer Learning with Variational Autoencoder). 于 2026-06-15 检索自 https://scholargate.app/zh/deep-learning/transfer-learning-variational-autoencoder · 数据集： https://doi.org/10.5281/zenodo.20539026