Machine learningDeep learning / NLP / CV
可解释变分自编码器
可解释变分自编码器（Explainable Variational Autoencoder, XVAE）在标准VAE框架的基础上，增加了使潜在空间可解释的技术：将潜在维度解耦，使每个维度对应一个人类可理解的因素；或使用事后归因方法（SHAP、集成梯度）将重建追溯到输入特征。它保留了VAE的生成能力，同时增加了科学和高风险应用所需的透明度。
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来源
- Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. In Proceedings of the 2nd International Conference on Learning Representations (ICLR 2014). link ↗
- Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., & Lerchner, A. (2017). beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework. In Proceedings of the 5th International Conference on Learning Representations (ICLR 2017). link ↗
如何引用本页
ScholarGate. (2026, June 3). Explainable Variational Autoencoder (XVAE / Interpretable VAE). ScholarGate. https://scholargate.app/zh/deep-learning/explainable-variational-autoencoder
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