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自监督变分自编码器

自监督变分自编码器（SS-VAE）结合了标准VAE的生成式潜在空间学习与自监督的代理任务（如对比增强、掩码重构或旋转预测），旨在从无标签数据中学习更丰富、更解耦的表示，而无需任何手动标注。

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自监督变分自编码器
微调变分自编码器生成对抗网络多模态变分自编码器自监督卷积神经网络半监督变分自编码器变分自编码器可解释变分自编码器自监督生成对抗网络

来源

  1. Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. In Proceedings of the 2nd International Conference on Learning Representations (ICLR 2014). link ↗
  2. Liu, X., Zhang, F., Hou, Z., Mian, L., Wang, Z., Zhang, J., & Tang, J. (2021). Self-Supervised Learning: Generative or Contrastive. IEEE Transactions on Knowledge and Data Engineering, 35(1), 857–876. DOI: 10.1109/TKDE.2021.3090866 ↗

如何引用本页

ScholarGate. (2026, June 3). Self-supervised Variational Autoencoder (SS-VAE). ScholarGate. https://scholargate.app/zh/deep-learning/self-supervised-variational-autoencoder

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被引用于

可解释变分自编码器自监督生成对抗网络半监督变分自编码器

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ScholarGate — Self-supervised Variational Autoencoder (Self-supervised Variational Autoencoder (SS-VAE)). 于 2026-06-15 检索自 https://scholargate.app/zh/deep-learning/self-supervised-variational-autoencoder · 数据集： https://doi.org/10.5281/zenodo.20539026