Machine learning
变分自编码器
变分自编码器（Variational Autoencoder, VAE）是由 Diederik Kingma 和 Max Welling 于 2014 年提出的深度生成潜在变量模型，它将数据编码为潜在空间中的概率分布，并从该分布中采样以生成新样本。它用于数据生成、异常检测和特征学习。
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来源
- Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR). link ↗
- Higgins, I. et al. (2017). beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework. International Conference on Learning Representations (ICLR). link ↗
如何引用本页
ScholarGate. (2026, June 1). Variational Autoencoder (VAE). ScholarGate. https://scholargate.app/zh/deep-learning/variational-autoencoder
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