Machine learningDeep learning / NLP / CV
半监督变分自编码器
半监督VAE（M2模型）是一种深度生成方法，它在一个原则性的概率框架内，利用有标签和无标签的样本，联合学习输入的潜在表示和分类器。该方法由 Kingma 等人于2014年提出，通过让生成模型解释无标签观测值，即使在标签稀疏的情况下也能实现准确分类。
阅读完整方法
仅限会员
登录使用免费账户登录即可阅读本节。
Method map
The neighbourhood of related methods — select a node to explore.
来源
- Kingma, D. P., Mohamed, S., Rezende, D. J., & Wierstra, D. (2014). Semi-supervised learning with deep generative models. Advances in Neural Information Processing Systems (NeurIPS), 27, 3581–3589. link ↗
- Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR 2014). link ↗
如何引用本页
ScholarGate. (2026, June 3). Semi-supervised Variational Autoencoder (M1/M2 Generative Model). ScholarGate. https://scholargate.app/zh/deep-learning/semi-supervised-variational-autoencoder
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.
- 生成对抗网络深度学习↔ compare
- 自监督变分自编码器深度学习↔ compare
- 半监督卷积神经网络深度学习↔ compare
- 半监督式 Transformer深度学习↔ compare
- 迁移学习与变分自编码器深度学习↔ compare
- 变分自编码器深度学习↔ compare