Machine learningDeep learning / NLP / CV
弱监督变分自编码器
弱监督变分自编码器（WS-VAE）通过整合部分、带噪声或粗粒度的监督信号（例如众包标签、启发式规则或程序化标注）来扩展标准的VAE生成框架，以指导潜在空间学习，而无需完全标注的数据。它广泛应用于计算机视觉、自然语言处理和生物医学领域，这些领域中完整的真实标签成本高昂或不可用。
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. & Welling, M. (2014). Auto-Encoding Variational Bayes. In Proceedings of the International Conference on Learning Representations (ICLR 2014). link ↗
- Kingma, D. P., Mohamed, S., Rezende, D. J. & Welling, M. (2014). Semi-supervised learning with deep generative models. In Advances in Neural Information Processing Systems (NeurIPS 2014), 27. link ↗
如何引用本页
ScholarGate. (2026, June 3). Weakly Supervised Variational Autoencoder (WS-VAE). ScholarGate. https://scholargate.app/zh/deep-learning/weakly-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.
Compare side by side →